Determinants of Stock Price Volatility: A Literature Review

Purpose: This paper reviews the theoretical background and the empirical results of the stock price volatility determinants under three categories: macroeconomic, company-specific fundamentals, and behavioral factors. Methodology: Previous empirical and theoretical articles on volatility determinants were compared to identify the similarities and differences of the findings. The systematic literature review followed to review the articles published in English between 1930 and 2021. Design: A critical literature review was performed by comparing the findings of previous studies based on the development status of the market. We discuss determinants of stock price volatility. Determinants include behavioral (non-fundamental) factors and macro-economic factors such as GDP, Inflation, Interest Rate, Money Supply, and Exchange Rate. In addition to Earnings and Dividend Payments have been considered under company-specific fundamentals. Findings: It was found that there is no agreement between the studies on the macro-level and micro-level determinants of stock volatility. This empirical inconsistency is substantial in GDP, Inflation, Money Supply, Exchange Rate, Earnings, and Dividend Payments. The interest rate is the only determinant that shows moderate inconclusive empirical results. However, behavioral determinants appear to be significance consistency in determining the stock price volatility. Originality: This article reviews the theoretical and empirical background of stock volatility determinants since there is no single article for reviewing theoretical and empirical results. In a single paper, we provide evidence relating to the impact of macroeconomic, company-specific, and behavioral factors on stock price volatility. Research Directions – Future research is needed to examine the reason for empirical inconsistency in volatility determinants. A systematic literature review is essential.


I. Introduction
The movements of stock prices highly depend on the respective economic conditions (Chen, Roll, & Ross, 1986). If the economy is performing well, it may lead to a bullish capital market, whereas its poor performance may carry out a bearish capital market. Changes in macroeconomic fundamentals impact stock prices irrespective of the firm's industry. As an example, raising the inflation rate decreases the purchasing power of the customers, leading to lower revenue for the firms (Ball & Romer, 2003). After that, the stock price may decline slightly according to the degree of inflation impact. This common phenomenon was theoretically backed by Arbitrage Pricing Model (APT) introduced by Ross (1976). The APT extends the multifactor model capturing the linear impacts of risk factors on asset returns. Macroeconomic impact has been identified as risk factor in asset return leading to price variability. Different assets have their own degree of risk premiums based on the sensitivity of the asset return on macroeconomic fundamentals. In addition to that, company-specific fundamentals also influence stock prices, like return on assets, future expected cash flows, earning power, etc. However, macroeconomic factors primarily determine the overall market performance.
The general idea is that the variability in stock prices is subject to the performances and expectations of general economic conditions. Otherwise, the stock prices have to behave arbitrarily, which is an unrealistic assumption in a rational market. Therefore, there is no argument saying that macroeconomic determinants have no impact on stock price volatility. Even though this theoretical argument has been accepted worldwide, the empirical evidence is inconsistent.
Many studies have identified that macroeconomic variables significantly determine stock price volatility. There are also empirical shreds of evidence stating that macroeconomic variables do not affect the stock price volatility. This empirical inconsistency is similar when the macroeconomic variables are considered individually based on different perspectives, like a market vice, the direction of the influence, etc. Moreover, the same result can be obtained for company-specific fundamentals. Under the rational investment decision-making assumption, both macro and micro (company-specific) level volatility determinants are considered as stock fundamental variables. However, nonfundamental factors (behavioral) deliver consistent empirical results for determining stock volatility. It can be identified that there is a substantial inconsistency in the empirical evidences of fundamental stock volatility determinants even though they are backed by theoretical arguments as significant determinants.
Nevertheless, nonfundamental factors appear as significant volatility determinants. This paper aims to identify the existing gaps relating to the determinants of the stock price volatility. The study mainly focuses on the consistency of the research results between different authors and markets. Additionally, the differences in the directions of the variable impact are presented to explain the similarity and disparity of the empirical results.

II. Methodology
This study reviews theoretical and empirical findings of stock price volatility determinants. Relevant theoretical studies were considered for emphasizing the theoretical background, whereas a substantial part of empirical studies was reviewed for identifying the significance of the volatility determinants, which are backed by the theoretical arguments.
The systematic literature review (SLR) method was followed to review the theoretical and empirical background of the stock price volatility determinants. The SLR is a successful method for identifying, evaluating, and summarizing the findings of previous studies. The relevant articles have been collected from the popular bibliographic electronic databases (Scopus, JSTOR, Taylor & Francis, Emerald, etc.) published in English from 1930-2021. The relevant articles have been discovered using selected keywords i.e; stock price volatility, macroeconomic determinants, companyspecific factors, and behavioral factors. Therefore, author was able to access a wider range of previous studies appropriate to the given objective.
The empirical inconsistency was assessed based on the border market perspective with three major market categories of Develop Markets, Emerging Markets, and Developing Markets. The Emerging and Developing markets have been considered as a single category while keeping the Develop market to the other end. The market classification is identified based on the All-Country World Index (ACWI) maintained by Morgan Stanley Capital International (MSCI).

III. Literature Review and Discussion
Stock price volatility is not an independent determinant; it is subject to the variability of other explanatory variables. Mainly, two factors cause on stock price volatility, changes in future cash flow expectations and investor's noise trading. These cash flow expectations are largely driven by macroeconomic or microeconomic fundamentals. On the other hand, these variables are financial variables that have a direct or indirect impact on stock price dynamics. The noise trading involves with non-fundamental factors. Technically we call them as behavioral factors of investors, in other words, non-financial variables. Therefore, these stock volatility determinants can be broken down into three categories, Macroeconomic Determinants, Company fundamentals (Microeconomic Variables), and Behavioral Determinants. Hereafter, the theoretical background and empirical background of the above categories are discussed one by one individually.

Macroeconomic Determinants
The stock market operates under the financial system of a country; whereas the financial system is a blend of economic activities and variables. Conceptually, price behavior of the stock market depends on the current and future economic fundamentals, since firms' earning projections are mainly driven by the economic health. Stock volatility is a mix of several macro determinants with different degrees of influence depending on the nature of the business of the subject stock. Point is that the future cash flows of a firm should be connected with the influential macro determinants to capture the future business conditions and stock behavior. Ross (1976) has introduced the APT theory for determining the financial asset return. This is the most prominent theory for the relationship between macroeconomic variables and stock volatility. APT is a multifactor model in order to capture the linear relationships between asset return and the variability of macroeconomic determinants. Moreover, the APT theory is mainly driven by the basis of systematic risk which was initiated by Sharpe (1964). Systematic risk is a return concept for accumulating the general economic risk of an asset. Therefore, the APT is the best initiation theory for explaining the sensitivity of the stock price with respect to macroeconomic fluctuations. In addition to that, Fama & French (1995; has introduced a multifactor model for identifying the price volatility as a function of asset risk factors. These risk factors include a mix of company fundamentals as well as general economic variables. Therefore, the model has less explanatory power for emphasizing the macro determinants of stock price volatility. In conclusion, the APT is a well-known and simple explanation as a model and as a theory. However, empirical results do not provide consistent results as the theory says. Because, some studies emphasized that macro variables are significant, but other studies rejected the macroeconomic significance. Table 01 shows the comparison of empirical  inconsistency between Developed, Emerging, and Developing markets. It can be observed that the macroeconomic variables do not deliver consistent results in determining the stock price volatility irrespective of the market's development status some studies argued about the moderate impact of macro variables other than the significance-insignificance argument. As an example, Chen (2009) conducted a study in USA and revealed that macroeconomic variables have moderate predictability power in stock dynamics. In his study, some variables are significant while others stay as insignificant variables. Further, he emphasized that the application of macroeconomic data is easier under recessionary conditions than under normal conditions. Similarly, Humpe and Macmillan (2009) have received the same result on research conducted in the same market and same period. Further, Chaudhuri and Smiles (2004) revealed that the stock volatility modeling is limited to the past macro data, and expected future macroeconomic data does not explain the current stock price.
The study of Asaolu and Ogunmuyiwa (2011) was utilized different statistical models to determine the significance of macroeconomic data. However, entire models provide the same result that is the insignificance of macroeconomic variables for modeling the stock variability. Therefore, the above findings concluded that there is a greater inconsistency of empirical results on macro variables in determining the stock volatility. It was observed that the authors have made greater weight on GDP, inflation rate, interest rate, money supply and exchange rate. Hereafter, it is presented the validity of those individual macroeconomic variables one by one.

Gross Domestic Production (GDP)
The Gross Domestic Product (GDP) is the total value of the final goods and services produced by a nation over a specified period of time (Brezina, 2012). This measure consists of four components, consumption, investment, government expenditures, and foreign demand (Dornbusch & Fischer, 1994). The future expectation of GDP is the main determinant of the nation's economic health. Further, this is the basic and initial factor in stock price valuation and prediction. A higher GDP value is a positive sign for a good business environment and higher cash flow expectations. However, the magnitude of the impact is different among the markets and firms. Theoretically, this relationship can be modeled in the APT theory or Multifactor models as a single risk factor in stock return expectation.
There are numerous empirical studies conducted for testing the validity of the GDP in determining the stock variability. As shown under the macroeconomic variables, the GDP also has inconsistence empirical results among Develop, Emerging and Developing markets (See Table 2).  Hussain et al. (2013) *The study was conducted in different markets with a mix of either Developed, Emerging and Developing markets.
Furthermore, this inconsistent nature of the empirical results can be recognized even within the same capital market. As an example, Kim (2003), Ratanapakorn and Sharma (2007), Gallegati (2008), and Humpe and Macmillan (2009) have identified the GDP as a significant variable in USA stock volatility. But studies of Chen (2009), Shiblee (2009), Madsen et al. (2013), Hossain and Hossain (2015), and Alexius and Spang (2018) rejected the GDP value for modeling and forecasting the stock prices in USA. Similarly, in India, GDP is a significant determinant in one context (Reddy, 2012) and insignificant in another context (Bhattacharya & Mukherjee, 2006;Sarkar, 2006). Further, this same significant (Maysami et al., 2004;Wongbangpo & Sharma, 2002) insignificant (Maysami & Koh, 2000) nature can be discovered in Singapore as well.
Moreover, the variability of this relationship is observed as a time-varying impact between GDP and stock volatility. That is, GDP has no impact in the short term, but, it has a strong relationship in the long term as a stock volatility determinant (Liu & Sinclair, 2008;Peng et al., 2009). However, the literature for the above observation is limited to the Chinese capital market. Finally, it can be concluded that there is a strong inconsistency in empirical evidences about GDP and stock volatility relationship among countries, time periods, and regions.

Inflation Rate
The inflation Rate mainly causes the consumption-ability of the people. In addition to that, it influences the financial system through the borrowing and lending rate. As a result, an investor never forgets to accommodate the future inflation rate for their investment decision-making process. Hence, there is a strong linkage between inflation rate and stock price volatility.
What would be the relationship between inflation and share volatility? There are several theoretical arguments for explaining the above relationship. The Long-Run Inflation Neutrality (Monetary Super-Neutrality) says that climbing the inflation trend rate (as a result of the money supply) has no impact on long-term share prices in the real term. Nevertheless, general well-known theories say that a bullish pattern of inflation causes a bearish impact on real stock prices in the long term. According to Modigliani and Cohn (1979), this negative long-term relationship is due to the "Inflation Illusion" which suffers by market participants. This inflation illusion arises because of a general behavioral bias of investors when they are unable to recognize the changes in nominal interest rate whether due to a fundamental change or inflation change. Therefore, investors are unable to differentiate between nominal and real interest rates when selecting the proper discount rate for stock valuation. Modigliani and Cohn (1979) identified this relationship in USA during the period between 1960 and 1970. Subsequently, Feldstein (1982) has added more explanation for the above inverse relationship. He developed a market model and emphasis that the negative pattern is due to the way of tax treatment on depreciation cost and capital gains. Both Modigliani and Cohn and Feldstein's attempts are well profound studies for the theoretical long-run inverse relationship between stock prices and inflation rates.
This inverse relationship was confirmed by Fama (1981) with a strong explanation based on equity fundamental factors. He emphasized that this negative pattern is a result of a positive relationship between stock return and real fundamental factors which are affected by the stock prices. However, the Famas' argument is more practicable and a little ahead of the other two explanations.
The applicable discount rate is an essential input for better stock valuation and investment decision-making. However, the discount rate is impacted by the current and expected inflation rate. But, the degree of impact will depend on the nature of the firm's operations. As an example, a retail business is more sensitive to the inflation rate than a service firm providing business consultations. Therefore, the applicable discount rate of retail stock is strongly linked with inflationary changes. This argument is in line with the above-explained Modigliani and Cohn (1979), Fama (1981), and Feldstein (1982) arguments and can be simply illustrated from the Dividend Discount Model (DDM) as follows (Formula 1).

= ⁄
Where, P denotes the value of a stock price, D denotes constant dividend payment and r is the required rate of return (RRR) of the stock. This RRR is a blend of risk premiums related to the subject stock. Assumed that given r value fully represents by the real rate of return and future expected dividends are subject to the inflationary rate of the economy. Hence, the RRR should be a nominal rate in order to discount nominal future dividend payments. Then, the nominal RRR is the sum of real rate and inflation premium (r + i). As a result of that, the discount rate of the model becomes larger and P-value would be a lower amount. Therefore, when the price level is increasing, the stock price tends to erode in the short-run as well as the long-run.
In addition to the above argument, the ability of inflation hedge is also an important factor that is considered by an equity investor before the investment is made. When the stock prices fully reflect the fluctuations in price levels, investors are well confident with the particular market. Otherwise, they will withdraw the investment and find a better place for their investment with the inflationary hedge. Therefore, this stock price sensitivity on the inflation rate is essential for a good capital market. The given price reflection is totally depending on the information distribution among the market participants.
The explained inflation-stock price relationship is basically standing on the wellknown theory of the "Fisher Effect" introduced by Fisher (1930). This fisher effect describes the impact of the nominal interest rate from the inflation rate volatility, thereafter on stock return and investment behavior.
The theoretical relationship was tested by different researchers at different markets. However, the results are not similar and it is observed an empirical inconsistency, like identified in DGP determinant (See Table 3).  (2015) Khalid and Khan (2017) Bai (2014) Fahlevi (2019) *The study was conducted in different markets with a mix of either Developed, Emerging and Developing markets.
Further, the same result can be detected when the studies are compared markets-vice individually. As an example, Rapach (2002), Rapach et al., (2005), He (2006), and Shiblee (2009) (2012) argued inflation as an essential determinant for modeling and predicting the stock volatility. This US uneven relationship pattern was identified by  in his research study separately.
In addition to that Anari and Kolari (2001) conducted a study to test the relevancy of the inflation rate with respect to different developed stock markets in USA, UK, Canada, Japan, Germany, and France. He concluded that the inflation is a powerful determinant in all six (6) countries. But, this result was rejected by Rapach (2002) and Rapach et al. (2005), specifying that the inflation factor does not provide any significant influence for stock volatility in all six countries. Similarly, this nature of the inflation influence is the same in China, Indonesia, New Zealand, and Pakistan (Refer Table 4 for a summary of references).  (2002) Fahlevi (2019) New Zealand Gan et al. (2006) Rapach (2002) Pakistan Khalid and Khan (2017) Saleem et al. (2013) Further, this variability is observable in between different lime lags being considered.
As an example, Crosby (2001) revealed a short-run significance and long-run insignificance relationship, while Kwofie and Ansah (2018) argued a long-run significant and short-run insignificant relationship.

Interest Rate
The interest rate is a vital factor in every economy as a variable or as a tool. Central banks use interest rate for controlling lending and borrowings of an economy in order to achieve its expected monetary targets. The viability of interest rate has a direct impact on the capital market. This was initially explained by John Maynard Keynes during 1930 as the Keynes's Theory of Money. This well-known theory says a negative relationship for investment money demand with respect to the interest rate (see figure I).
In elaboration, investors prefer to invest in the equity market when the interest rate is low since they expect a higher return at the lowest risk. Figure I shows the graphical illustration of this explanation.    (2004) Kurihara (2006) Korkeamäki ( Moreover, the result is similar when the literature compares in individual marketvice. This can be observable in Australia, Italy, India, Indonesia, Japan, and UK (Refer Table 6 for a summary of references). Further, this evidence has been amplified by He (2006), which is a study for identifying the effects of monetary policy on the stock return in USA during the last longer period. Accordingly, the interest rate-stock price relationship does not provide consistent results in USA during the past four decades. Further, this inconsistent market-vice relationship was recognized by some authors within their same study (Abugri, 2008;Ferrer et al., 2016;Hyde, 2007).  (2004) Italy Rapach et al. (2005) Hyde (2007) India Reddy (2012) Andries et al. (2014) Mishra (2004) Indonesia Wongbangpo and Sharma (2002) Gupta et al. (2001) Japan Rapach et al. (2005) Alam and Uddin (2009) Kurihara (2006) UK Rapach et al. (2005) Hyde (2007) In addition to that, the empirical variability appears even between long-term and shortterm rate considerations. Hamrita and Trifi (2011), Olugbode et al. (2014), and Jayashankar and Rath (2017) have revealed that the short-term rate does not have an impact on stock prices while the log-term rate has a significant influence on stock variability. Conversely, according to Ratanapakorn & Sharma (2007), the interest rate is a significant determinant for stock volatility modeling irrespective of the shortterm and long-term rate difference, but the impact has different directions (Positive and Negative). However, all these evidences prove the empirical inconsistency of the inflation rate with respect to the stock price volatility impact.

Money Supply
The monetary policy influence on the equity market was highlighted in the above two variables (Inflation Rate/ Interest Rate). The Money Supply is the main exogenous variable that determines the monetarist variables in an economy. This is totally an independent determinant of the Central Bank in order to align the monetary policy targets with economic expectations. Then, the investigation of money supply on stock market performance is important in both theoretical and empirical perspectives.
According to the Keynesian theory, higher money flow causes higher price levels of an economy. As discussed previously, investors add inflation premium into the discount rate which is used to value the expected cash flows. When the inflation rate is higher, the discount rate also tends to increase, ultimately the value of a share price would be a lower amount. Therefore, this Keynesian theory has found a negative influence from money supply on stock prices. However, this argument was contradicted by the same Keynesian theory from a different viewpoint. Because Keynesian Money Demand Thought has a positive impact from money flow. In other words, higher money flow leads to lower interest rates and thereafter, higher demand for high return equity securities. Accordingly, the stock volatility increases as a result of the trading rush from the huge demand. Hence, the theory of Keynesian has a partial positive and partial negative explanation for money demand, but the variable significance has been confirmed under both views. The second explanation (Positive Relationship) is also the grounded rationale for the negative relationship between interest rate and stock volatility as explain under the Interest Rate variable.
Further, this positive argument is in line with a modern concept of investment portfolio theory. It says investors re-balance their portfolio into interest-bearing money assets (like equities, bonds) from non-interestbearing money assets (like real assets, commodities). In addition to that, the higher money flow and lower interest rate have positive signals on expected corporate earnings and higher return. Thereto, it can be expected a positive impact from money supply on the stock volatility dynamics. However, the Friedman and Schwartz (1963) initiation is totally different from the above portfolio theory view. Their argument is, the narrow money supply expands the business actives and expected future cash flows, but this has limited applicability in the real environment. Finally, it can be seen a debatable theoretical background in money supply and stock volatility relationship, however, the positive influence is strong and dominant among other battles.

Market Money Supply is Significant Money Supply is Insignificant Develop Market
Shiblee ( Empirical evidences have the same nature of inconsistency that was identified under the previous cases. There are favorable evidences on significance of money supply on stock price volatility while other studies do not provide reliable evidences on money supply in determining the stock price volatility. This is not different even the results are compared between Develop, Emerging and Developing markets (See Table 6). Similarly, this variability appears among the individual markets when the empirical studies are compared on the market basis. For instance, Ratanapakorn and Sharma (2007), Shiblee (2009), and Caginalp and Desantis (2011) argued that money supply as a significant determinant for modeling the USA stock volatility. Conversely, some evidences have provided that the money supply as an insufficient variable in USA equity market (Chen, 2009;Durham, 2001;Humpe & Macmillan, 2009;Rahman & Mustafa, 2008;Rapach et al., 2005;Wong et al., 2006). Similar results can be obtained for the New Zealand equity market (Durham, 2001;Gan et al., 2006).
In addition to that, this inconsistency is observable with respect to the sign of the relationship (Positive/ Negative) even if the money supply stays as a significant determinant. According to Wongbangpo and Sharma (2002), Indonesia and Philippines have a negative impact while Singapore, Thailand, and Malaysia have a positive influence on share price behavior. Further, this sign difference can be recognized within the same territory. As an example, Hussain et al. (2013) argued a positive relationship and Almutair (2015) argued a negative relationship in Pakistan.
Moreover, the empirical inconsistency is possible when the results are compared based on the global viewpoint (Durham, 2001;Rapach et al., 2005). Finally, all these arguments lay a clear-cut conclusion that money supply has a questionable impact on stock price volatility.

Exchange Rate
The above variables explain the deviation of a country's internal macroeconomic environmental change. The external economic bounces haven't been taken into the discussion. Therefore, the Exchange Rate is the most suitable variable for undertaking the above external economic impact. Additionally, the exchange rate has effectively recognized the link between internal and external environmental changes. Thereto, theoretical and empirical investigation of Exchange Rate impact is vital for better stock volatility modeling.
A strong theory for the Exchange Rate-Stock Price relationship is unobservable among the academic literature. This issue arises because different exchange rates are available in different currencies and the sensitivity of the currency is different based on the foreign exposure of the respective country. However, there are some arguments existing based on the rational existing theories.
As Interest Rate Parity conditions, the exchange rate and the interest rate have an inverse relationship. This foundation lays to build up a relationship between exchange rate and stock price volatility. Since the interest rate has a negative impact on stock price, the Interest Rate Parity condition can be merged to realize a positive relationship between Exchange Rate and Stock Prices. The Figure  II illustrates the given argument graphically.

Figure 2. Exchange Rate and Stock Price Relationship
The former theories have based on the fluctuations of the current exchange rate on the adjustments of the stock prices. However, modern concepts assume appreciation and depreciation are vital for expected exchange rates than the current rates.

Emerging and Developing Markets
Wu ( The empirical evidences have the same inconsistent pattern as observed stated under the previous variables. This inconsistency was identified by different authors even within their own work (Abugri, 2008;Chue & Cook, 2008;Diamandis & Drakos, 2011;Granger et al., 2000;Inci & Lee, 2014;Nath & Samanta, 2003;Pan et al., 2007;Ramasamy & Yeung, 2002). According to Ramasamy and Yeung (2002), Nath and Samanta (2003), Pan et al. (2007), Abugri (2008), Chue and Cook (2008), and Diamandis and Drakos (2011) this empirical variability highly exists in the Emerging Markets and Asian Markets. The Table 7 shows the summary of the comparison of empirical evidences on Exchange Rate in determining the stock price volatility.
However, when the literature compares with in the same market, the uneven results can be observed (Refer Table 8 for a summary of references). In addition to that, the direction of the exchange rate impact also does not have consistent results. As an example, Kim (2003) and Aydemir and Demirhan (2009) have emphasized a negative influence of the exchange rate on the stock volatility, while Tian and Ma (2010) and Khalid and Khan (2017) argued a positive relationship between them. Therefore, these facts and comparisons are more than enough to conclude that the exchange rate has a variable impact on stock price volatility.

Company Fundamental Factors
Examine the microeconomic environmental change is important as identifying the macroeconomic impacts, for the intention of volatility modeling. Company fundamental factors represent the micro environmental behavior of a subject market or a firm. These are the inside determinants for stock price dynamics which consider by an investor before making the investments. On the other hand, company fundamentals are the bottomline reflections of macro-environmental impacts. As an example, higher GDP expectation appears through the higher earnings expectations. The significance of the company fundamentals was tested by several authors and found that company fundamentals better explain the stock volatility (Lamdin, 2001;Mgbame & Ikhatua, 2013;Mylonakis et al., 2012;Pirie & Smith, 2008;Zhu & Niu, 2016).
Among the other specific fundamentals, earnings and dividend payments are two major insider fundamentals that highly influence the investors' decision-making. This is agreed since investors are too sensitive to the earning power of the stock as well as the distribution ability. It is pointless to invest in a company without having a good return from the investment.
Two basic valuation models individually accommodate the above-mentioned earnings and dividends into the share price. The DDM model captures the expected dividend payments and assumes that future dividend payments are the best estimates for the current value of a share. In addition to that PE multiple relies upon the future earning ability irrespective of the distribution of the stock.
However, the impact of the above two variables was identified one by one separately.

Earnings
The earnings of a firm have been extensively considered by market participants before the investment is made. This comprises two major components, current earnings, and future earnings. However, expected future earnings are dominant and important than the current earnings for price forecasting and volatility modeling.
Earnings measures the firm's ability to generate positive cash-flows from the business for distribution and investment. The main earning driver is the firms' revenue and this erodes from the cost of the business. Generating a higher revenue does not indicate good earnings, rather than it is required to minimize the cost of doing business. Sometimes earnings have different values depending on their definition. As an example, accounting earning has a different value than the earning calculated for discounted cash flow analysis. However, accommodating accurate earning expectations helps for better price forecasting and measuring (Bhojraj & Lee, 2002;Chen & Shen, 2009;Liu et al., 2002).
Measurement and prediction of future cashflows is a challenging task due to the uncertainty in the business environment. If earnings have an optimistic nature, it increases the investor's expectations and confidence, resulting a higher volatility. Conversely, when the expected earnings are too low, less trading is occurred with minimal price volatility. Therefore, it can be identified a positive impact from the earnings and price 41 RANIL HEWAMANA ET AL.
volatility. However, this theoretical argument was tested by the plethora of empirical studies. Table 10. Market wise Earnings Impact

Market
Earnings is Significant Earnings is Insignificant Develop Market Marquering and Verbeek (2004 Empirical evidences have mixed results for stock volatility prediction and modeling irrespective of the development status of the market (See Table 9). This observation is not different from the observations of other macroeconomic fundamentals. Therefore, it is clear that empirical findings have a significant variability even with strong theoretical arguments.

Dividends Payments
Mainly, the dividend is considered as the shareholder's profit and distribution of the investment return to its owners. Most of the time, the dividend is a portion of the business income and earnings, since managers wish to retain a part of earnings for future investment purposes. Larger allocation on investments makes positive expectations of business earnings at the expense of return distributions. Therefore, there is a trade-off between dividend payment and investment retains. As a result of that, dividend is a vital measurement for both investors as well as managers.
Usually, dividends are made in terms of either cash or stocks. Payment of dividends is different not only to market vice but also firm vice. Gordon and Shapiro (1956) identified the DDM model and assumes that the present value of future dividend payments is the best estimate for equity price. According to DDM, the share price has an impact on the dividend policy of a firm, leading to a positive relationship. However, the influence of the dividend payment has a contradictory pattern both theoretically and empirically. Miller and Modigliani (1958) have initiated the relationship between stock price volatility and dividend payment in their cost of capital model. Later, Black (1976) introduced a dividend puzzle by mentioning dividend is not an influential factor for measuring the company stock price since they are the owners of the firm. In other words, investors do not make any adjustments to the stock price whatever the dividend amount. Additionally, Black argued that equity holders do not prefer dividend payments due to tax expense on dividend receivables. This totally complies with the dividend irrelevance theory by Miller and Modigliani (1961). However, these dividend irrelevance and puzzle arguments have been completely rejected by DeAngelo et al. (1996), specifying that dividend is a reliable factor for stock price variability. Later, this relationship was developed and identified in several other concepts, such as stakeholders' theory, pecking order theory, agency cost, signaling theory, bird-in-hand fallacy, and clientele effect. However, the importance of the dividend payment is declining gradually and it will disappear in the future (Skinner, 2008).

Market Dividends is Significant Dividends is Insignificant Develop Market
Campbell and Shiller (2001 Further, the direction of the impact also shows either positive or negative significance on price volatility (Hussainey et al., 2011;Lashgari & Ahmadi, 2014;Suleman et al., 2011). Hence, the similar empirical inconsistent is observable in dividend payments as identified under the previous determinants. This nature was identified separately by Camilleri et al. (2017), specifying that dividend payments have different results on volatility measurement.

Behavioral Determinants
Usually, Investors use microeconomic and macroeconomic fundamental factors to describe the stock price volatility. This method is under the rational investment decision-making process and assumes that all investors are rational decision-makers. However, investors make irrational decisions based on their behavioral biases. Behavioral biases are common to all investors with different degrees of influences depending on their personal cognitive and emotional thinking. In modern finance theory, the rational investor assumption is no longer valid and behavioral determinants also are too important for investigating the stock volatility behavior like fundamental factors.
Even though more advanced asset pricing models are available it is difficult to accommodate correct micro and macro fundamental factors in order to get the right value of the stock due to the lack of knowledge and/or data (Corredor et al., 2015). In other words, stocks that are hard to value and vulnerable for speculation subject to the market sentiment (Kumari & Mahakud, 2015). Hence, the irrational behavior is possible in each market irrespective of the market efficiency identified by Fama (1965). As Black (1986) stated, identifying behavioral factors is the best way to describe the impact of the investors who use nonfundamental factors for investment decisions.
Investors who use non-fundamental factors are technically called as "Noise Traders". Noise trading decisions are driven by herding behavior, loss aversion, status quo, and overconfidence, etc. According to Kumari and Mahakud (2015), the actions of noise traders have a serious influence when the share price deviates from its intrinsic value. Therefore, the market is subject to volatile frequently on the basis of the sensitivity of behavioral biases. This Kumari and Mahakud argument is totally in line with Selden (1912). He also recognized that investors' psychological impact is reflected and mirrored by the price dynamics according to the mental attitude of buyers and sellers. Shefrin and Statman (1994) have developed a behavioral capital asset pricing theory in order to explain the relationship between asset prices and behavioral biases. Accordingly, if the bullish sentiment is more dominated among the traders, the price moves above the intrinsic value due to the larger number of buying orders than selling orders. Conversely, prices pull below the intrinsic value, when the bearish sentiment is comparatively dominated in the market. Both bullish and bearish noise trading can make stock markets more volatile (Baker & Wurgler, 2006, 2007. A plethora of authors has confirmed that nonfundamental factors are highly significant in stock volatility modeling and prediction (Baker & Wurgler, 2006). For instance, the studies of De Long et al. (1987), Lee (1998;, Stracca (2004), Coval and Shumway (2005), Kengatharan and Kengatharan (2014), and Daniel et al. (2020) has longestablished that behavioral factors are more influential for price movements during the last four (04) decades. This significance highly appears in less developed markets than developed markets (Baker & Wurgler, 2006;Corredor et al., 2015;Schmeling, 2009). The influence of behavioral factors is not like the empirical evidences identified in previous cases (Macroeconomic/ Company Fundamental Factors), since there is a consistent significant nature in empirical studies.

V. Conclusion
It is clear that there is an empirical inconsistency in fundamental stock volatility determinants. All selected fundamental factors have inconclusive results on their influence in determining the stock volatility behavior irrespective of the development status of the market (Develop, Emerging and Developing market). This inconsistency is highly significant in terms of GDP, Inflation, Money Supply, Exchange Rate, Earnings, and Dividend Payments. Interest Rate is the only variable which has moderate inconsistence empirical results. However, most of non-fundamental factors deliver consistent conclusions for determining the stock price volatility dynamics. The significance of the non-fundamental factors is strong in Emerging and Developing markets than the Develop markets.
However, the non-fundamental factors (behavioral) have consistent results while fundamental factors are exhibiting this inconsistent influential problem. Therefore, investors are more confident with irrational decision making over rational decisions which are backed by fundamental factors. Generally, investors tend to behave in an irrational (behavioral) manner as a result of either inability to adopt a proper valuation or unavailability of correct data at right time. This data unavailability issue dominates than the other reason since the proper valuation problem can be avoided with the help of an investment advisor or a stockbroker. Therefore, future research is needed for identifying the relationship between information distribution patterns and the stock volatility behavior in equity markets.