Drives Stock Returns Research Articles

Regulations, Bank Stability Measures and Stock Market

Regulations directed at bank stability affect the entire financial system because of the strategic role banks play in the economy. The impact of regulations on bank performance have been examined. However, the use of bank regulatory stability indicators and aggregate financial regulatory instruments to account for stock market returns in Nigeria post-global financial crisis is limited, which is the gap this study seeks to fill. Data for this study was collected from investigated banks’ audited annual reports, the Central Bank of Nigeria Statistical Bulletin and Nigerian Stock Exchange official daily price list. Thirteen (13) banks listed in the Nigerian Exchange Limited for the 2010 to 2020 were investigated. The Difference Generalized Method of Moments was applied to the dynamic model to examine the effect of regulations, bank stability indicators on the returns of bank stocks. The Arrelano and Bond procedure was used to test for autocorrelation in the regression output, while the J.statistic enunciated by Hansen was employed to evaluate the existence or otherwise of over/under constraints in the instrumental variables incorporated in the estimation tool. E-view 9.0 econometric computer application was used for the analysis. This study found that bank liquidity ratio, an indicator of bank stability in this study positively and significance determines stock market returns. Also, capital adequency ratio, another bank stability indicator in this study positively and significantly drives stock returns. Similarly, monetary policy rate, an indicator used by the Central Bank of Nigeria to regulate aggregate liquidity positively and significantly influence bank stock returns. This study also revealed that the prescribed cash reserve ratio used by the Central Bank to regulate the capacity of banks to grant credits is negatively and significantly related to stock returns in Nigeria. This implies that a rise in cash reserve ratio limits the ability of banks to create credits, thereby reducing interest income from loans, thus reducing bank earnings. These findings imply that: (i) Investors should incorporate bank stability indicators and aggregate monetary policy regulatory instruments in their investment strategies if they hope to achieved maximum returns on investment. (ii) The regulatory authority can influence stock market performance through the concurrent utilization of bank stability indicators enunciated in Basel regulatory accord and monetary policy instruments (iii) The use of Cash reserve ratio as the regulatory instrument is potent in ensuring bank stability but it has adverse effect on bank stock returns because of its effect on interest income and earnings through its limitation on credit creations. Therefore, the central bank should reconsider the current cash reserve ratio and make it more friendly for banks' credit creation.

Research on the Causes and Economic Consequences of Factor Crowding in China's Capital Market ——Take the Phenomenon of Grouping of Public Funds as an Example

The factors driving stock returns in investment activities have been a hot topic of research in modern finance. Previous researchers have proposed several multi-factor models for the drivers of stock returns in investment activities to improve the decision models of specific influencing factors in investment decisions. Based on research on public funds' grouping behavior in investment decision-making, this paper analyzes a series of decision behaviors of fund managers in the context of factor strategy crowding. Therefore, we identify the specific influences and behavioral contexts in which fund managers make investment decisions and provide a specific analysis of fund managers' investment decision opinions under factor strategy crowding, illustrating the economic mechanisms involved.

Firm quality and stock returns: Evidence from India

ABSTRACT Using data for 1 848 companies, we find that quality increases, not quality, drive stock returns in India. Profitability and safety seem to be relevant attributes for measuring quality. Our cross-sectional tests show that the role of quality in predicting returns is partially subsumed by momentum in short holding periods. Rational sources are not able to explain quality premiums. We find that quality premiums result from investor overreaction. At the same time, momentum profits are an outcome of investor underreaction, suggesting that investors pay more attention to fundamentals than past price trends. High investments by institutional investing may account for such behaviour.

Aggregate investor confidence, price momentum and asset pricing

ABSTRACT This article applies a new measure of aggregate investor confidence to explain the well-established factors driving stock returns in the asset pricing literature and on momentum returns. The aggregate measure extracts feedback impulses from stock market data that affect aggregate investor confidence. We find that aggregate investor confidence is positively associated with the profitability of momentum strategies and Fama–French’s small minus big (SMB) factor, providing empirical evidence in line with prominent behavioural models. Using a sample of data for the United States from 1927 to 2019, aggregate investor confidence requires around 3 months to notably affect momentum returns and remains statisticallct 3y significant for up to 16 months. Additionally, investors trade more and tilt their preference towards small market capitalization and growth stocks when confidence is high.

How Market Sentiment Drives Forecasts of Stock Returns

We reveal a novel channel through which market participants’ sentiment influences how they forecast stock returns: their optimism (pessimism) affects the weights they assign to fundamentals. Our analysis yields four main findings. First, if good (bad) “news” about dividends and interest rates coincides with participants’ optimism (pessimism), the news about these fundamentals has a significant effect on participants’ forecasts of future returns and has the expected signs (positive for dividends and negative for interest rates). Second, in models without interactions, or when market sentiment is neutral or conflicts with news about dividends and/or interest rates, this news often does not have a significant effect on ex ante or ex post returns. Third, market sentiment is largely unrelated to the state of economic activity, indicating that it is driven by non-fundamental considerations. Moreover, market sentiment influences stock returns highly irregularly, in terms of both timing and magnitude. This finding supports recent theoretical approaches recognizing that economists and market participants alike face Knightian uncertainty about the correct model driving stock returns.

How Market Sentiment Drives Forecasts of Stock Returns

We reveal a novel channel through which market participants’ sentiment influences how they forecast stock returns: their optimism (pessimism) affects the weights they assign to fundamentals. Our analysis yields four main findings. First, if good (bad) “news” about dividends and interest rates coincides with participants’ optimism (pessimism), the news about these fundamentals has a significant effect on participants’ forecasts of future returns and has the expected signs (positive for dividends and negative for interest rates). Second, in models without interactions, or when market sentiment is neutral or conflicts with news about dividends and/or interest rates, this news often does not have a significant effect on ex ante or ex post returns. Third, market sentiment is largely unrelated to the state of economic activity, indicating that it is driven by non-fundamental considerations. Moreover, market sentiment influences stock returns highly irregularly, in terms of both timing and magnitude. This finding supports recent theoretical approaches recognizing that economists and market participants alike face Knightian uncertainty about the correct model driving stock returns.

The Driving Forces of Stock Returns in Hong Kong

We comprehensively investigate what drives stock returns in Hong Kong stock market which has been consistently ranked as one of the most important markets for IPOs. We find that Hong Kong inflation rate is a systematic pricing factor across stocks after controlling for Fama-French three-factor. It is different from the U.S. market and other developed markets that the momentum, dividend yield, cash-flow yield, earnings yield, and return-reversal factors are not significant pricing factors for stock returns in Hong Kong. Our Fama-MacBeath (1973) regressions show that a stock’s value (cash-flow yield and book-to-market ratio) is the strongest predictor of stock returns in Hong Kong after controlling for market, value, and size factors and macroeconomic factors.

What Drives Stock Returns in Japan?

We investigate systematic factors driving stock returns and stock return predictability in Japan. We find that dividend yield, cash-flow yield, and industrial production are systematic pricing factors after controlling for market, value, and size while other macroeconomic factors are not. Value and size premiums become insignificant after adding the industrial production factor to market, value and size factors because the value factor captures the changing fundamentals of Japan's macroeconomic development. For predicting stock returns, our tests using Fama-MacBeth (1973) regressions accept the models of both factor and characteristics for a stock’s cash-flow yield, and a characteristics model for a stock's short-term reversal, dividend yield, and earnings yield.

What drives stock returns in Japan?

We investigate systematic factors driving stock returns and stock return predictability in Japan. We find that dividend yield, cash-flow yield, and industrial production are systematic pricing factors after controlling for market, value, and size, while other macroeconomic factors are not. Value and size premiums become insignificant after adding the industrial production factor to market, value, and size factors because the value factor captures the changing fundamentals of Japan’s macroeconomic development. For predicting stock returns, our tests using Fama and MacBeth’s (J Political Econ 71:607–636, 1973) regressions accept the models of both factor and characteristics for a stock’s cash-flow yield, and a characteristics model for a stock’s short-term reversal, dividend yield, and earnings yield.

What Drives Stock Returns in South Korea?

We comprehensively investigate what drives stock returns in South Korea stock market which has been one of the most important emerging markets. Unlike the USA market and other developed markets, the well-known cash-flow yield, dividend yield, earnings yield and return reversal factors are not significant pricing factors for stocks in South Korea while momentum factor has a significant and negative pricing premium. We also find that GDP growth is a systematic pricing factor across stocks after controlling for market, value, and size factors. The pricing premium of GDP growth is significantly positive and consistent with Merton’s (1973) ICAPM because economic growth provides positive investment opportunities to investors. For cross-sectional stock return predictability, our Fama-MacBath (1973) regressions show that a stock’s book-to-market ratio, cash-low yield, dividend yield and return reversal are significant predictors at 1% level after controlling for market, value, size, momentum and GDP factors.

Do the size, value, and momentum factors drive stock returns in emerging markets?

This paper investigates the size, value, and momentum effects in 18 emerging stock markets during the period 1990–2013. We find that size and momentum strategies generally fail to generate superior returns in emerging markets. The value effect exists in all markets except Brazil, and it is robust to different periods and market conditions. Value premiums tend to move positively together across different markets, and such inter-market comovements increase overtime and during the global financial crisis.

Modeling spatial and temporal dependencies among global stock markets

An intensive analysis of the dependence structure among stock markets is invaluable to financial experts, policy makers, and academic researchers, providing them with important implications for portfolio management, policy-making, and risk assessment. This paper proposes a novel spatiotemporal model to both examine global stock market linkages and investigate what drives stock returns. The newly introduced model allows us to go beyond conventional correlation analyses confined to studying pairwise relationships and seems to be more suitable for detecting the dependence structure of high-dimensional financial time series. Moreover, a new copula-based approach to define the spatial weight matrix is presented that is based on the construction of a dissimilarity matrix using the Spearman's contagion index. To the best of our knowledge, this paper is the first to incorporate copulas into the definition of the spatial weight matrix. In addition, the maximum likelihood estimator of our model is derived, together with a Monte Carlo simulation study evaluating its performance compared to two other methods. Finally, the results demonstrate that our proposed measure of the spatial weight matrix, coupled with our model, performs very well in terms of capturing spatial and temporal dependencies among global stock markets, and that the relative values of conditional volatilities are also important factors in determining stock returns.

A Replication Study of Ball and Brown (1968): Comparative Analysis of China and the US*

Abstract We replicate Ball and Brown (1968) using current US and Chinese data. We demonstrate that the significant relation between annual earnings changes and annual stock returns documented in Ball and Brown (1968) extends and holds to recent US data over the period 1971-2011 and that stock prices continue to react with some delay to unexpected earnings. This association result is confirmed using Chinese data over the period 1995-2011. However, our analysis reveals a key difference in relative magnitude—the Chinese stock market responds much more strongly to good news, and much less strongly to bad news, than the US market. In addition, we examine alternative selections of samples and benchmark returns using Chinese data. Our results suggest that the smaller magnitude and drift of market reaction in China cannot be driven by pre-warnings of earnings, firms in “abnormal trading status”, timing of earnings announcements, or alternative choices of benchmark returns, although the magnitude difference is greatly reduced when the financial crisis of 2007-2009 is excluded. Overall, our results confirm that earnings drive stock returns in both the US and China, but the market reaction to bad earnings news is notably muted in China, suggesting that non-earnings factors are more important in China.

International transmission of stock market movements: an adaptive neuro-fuzzy inference system for analysis of TAIEX forecasting

This study aims to examine the fundamental forces driving stock returns and volatility across the international stock markets. Logistic regression analysis is used to investigate possible highly correlated among 9 international stock markets with stock market of Taiwan. Afterward, the highly correlated stock indices with Taiwan would be selected as the input variables of adaptive network-based fuzzy inference system (ANFIS) model to predict stock prices and their direction of Taiwan Stock Exchange Capitalization Weighted Stock Index. The experimental results of the proposed model are contrasted with other models including the first-order model (Chen in Fuzzy Sets Syst 81(3):311–319, 1996), weighted fuzzy time series model (Yu in 349:609–624, 2005), simple neural network model (Huarng and Yu in Phys A 363(2):481–491, 2006), multivariate model (Huarng et al. in J Travel Tour Mark 21(4):15–24, 2007), ANFIS with volatility causality (Cheng et al. in Neurocomputing 72(16–18):3462–3468, 2009), ANFIS with AR model (Chang et al. in Appl Soft Comput 11:1388–1395, 2011), and artificial bee colony-recurrent neural network model (Hsieh et al. in Applied Soft Computing 11:2510–2525, 2011). Finally, the proposed model produces with lower inaccuracy rate and offers higher direction preciseness than above previous models. The benefit of this methodology depended on its application of a hybrid approach to predict the stock prices and direction with higher accuracy.

How Do Managers Move Firm Value? Cash Flows vs. Expected Returns

Stock prices are mechanically driven by changes in either expected cash flows or discount rates (expected returns). Fundamentally firm values move with managerial decisions. We explore through which channel managers commonly affect their firms’ value. A variance decomposition analysis indicates that when managers have a greater degree of discretion – i.e., when CEO compensation and ownership are high, and in unregulated and heterogeneous industries – cash-flow news becomes significantly more important in driving stock returns, while expected - return news becomes significantly less important. This suggests that managers move firm value mainly by changing expected cash flows and not firm risk.

Illiquidity and Stock Returns: Evidence from the German Stock Market

This paper examines the impact of illiquidity on equity returns on the German stock market. Since illiquidity has many facets, we cover the whole spectrum of illiquidity measures: trading speed, trading costs, trading quantity, and price impact. Based on these illiquidity measures we construct factor mimicking portfolios that capture the risk of illiquidity. Our findings provide evidence that illiquidity drives stock returns and entails a significant risk premium independent of the measure chosen. Additionally, we investigate the link between size and illiquidity and tackle the question if size proxies for illiquidity.

Empirical Analysis of Affine vs. Non-Affine Variance Specifications in Jump-Diffusion Models for Equity Indices

How to model the variance process driving stock returns is a major research questions in finance. The specification of a variance model has implications for, e.g., risk management decisions, portfolio allocation or derivative pricing. This paper analyzes several crucial questions for setting up a variance model. (i) Are jumps an important model ingredient even when using a non-affine specification? (ii) How do affine specifications perform when compared to non-affine models. (iii) How should non-linearities be modeled? We find that, first, jump models clearly outperform pure stochastic volatility models. Second, non-affine specifications outperform affine models, even after including jumps. And finally, we find that the polynomial specification of the drift term, that has also been used in short rate models, is the best non-affine model under consideration.

Risks of investing in the Russian stock market: Lessons of the first decade

The modern history of the Russian stock market has mirrored ups and downs of the country's transition as well as swings in investor perceptions. In this paper, we describe the evolution of the Russian stock market over its first decade, with particular attention to the risk factors driving stock returns. First, we analyze the development of the institutional infrastructure and dynamics of the market's size and liquidity measured by the number of listed and traded stocks, depositary receipts and IPOs as well as trading volume in the local stock exchanges and abroad. Then, we examine major political and economic events, which influenced the investor perceptions of the country risk and were reflected in stock prices. Finally, we carry out quantitative analysis of risk factors explaining considerable time and cross-sectional variation in Russian stock returns. We document a significant role of corporate governance, political risk, and macroeconomic risk factors, such as global equity markets performance, oil prices, and exchange rates, whose relative importance varied a lot over time.

Conditional correlations in the returns on oil companies stock prices and their determinants

The identification of the forces that drive stock returns and the dynamics of their associated volatilities is a major concern in empirical economics and finance. This analysis is extremely important for determining optimal hedging strategies. This paper investigates the stock prices’ returns and their financial risk factors for several integrated oil companies, namely Bp (BP), Chevron-Texaco (CVX), Eni (ENI), Exxon-Mobil (XOM), Royal Dutch (RD) and Total-Fina Elf (TFE). We measure the actual co-risk in stock returns and their determinants “within” and “between” the different oil companies, using multivariate cointegration techniques in modelling the conditional mean, as well as multivariate GARCH models for the conditional variances. The distinguishing features of this paper are: (i) focus on the determinants of the market value of each company using the cointegrated VAR/VECM methodology; (ii) specification of the conditional variances of VECM residuals with the Constant Conditional Correlation (CCC) multivariate GARCH model of Bollerslev [(1990) Review of Economics and Statistics 72:498–505] and the Dynamic Conditional Correlation (DCC) multivariate GARCH model of Engle [(2002) Journal of Business and Economic Statistics 20:339–350]; (iii) discussion of the performance of optimal hedge ratios calculated with the DCC estimates. The “within” and “between” DCC indicate time-varying interdependence between stock return volatilities and their determinants. Moreover, DCC models are shown to produce more accurate hedging strategies.

Monetary policy and dividend growth in Germany: long-run structural modelling versus bounds testing approach

This study examines the long-run relationship between monetary policy and dividend growth in Germany. For this purpose, cointegration is tested for between both variables in the period 1974 to 2003. However, problems related to spurious regression arise from the mixed order of integration of the series used, from mutual causation between the variables and from the lack of a long-run relationship among the variables of the model. These problems are addressed by applying the bounds testing approach to cointegration in addition to a more standard long-run structural modelling approach. In principle, both procedures are capable of dealing with the controversial issue of the exogeneity of monetary policy vis-à-vis dividend growth. However, the structural modelling approach still leaves a certain degree of uncertainty about the integration properties of the interest rate and the dividend growth. Hence, one feels legitimized to refer to the bounds testing procedure and to conclude that in the longer term short-term rates drive stock returns but not vice versa.