Accounting variables and stock returns： Evidence from Istanbul stock exchange

The fundamental relationship between accounting variables and stock returns is a recurring theme in financial research. One of the major purposes of accounting is to help investors provide reliable, comparable and accurate information. If accounting data are informative about fundamental values and changes in values, they should be correlated with stock price changes. This study provides theory and evidence showing how accounting variables explain stock returns and examines the relationship between the stock returns and accounting variables of listed non financial companies in ISE-100 Indice for 2006-2008 period by using panel data methodology. Empirical analysis consists of 192 observations of 64 companies in years 2006-2008 to examine the effects of inventory, accounts receivable, gross margin, operating expense, return on assets, cash flow, leverage, liquidity, price/earnings, return on equity on stock returns. The results of the study confirm that the predicted roles of fundamental factors and stock returns are significantly related to gross margin, cash flow, leverage and equity variables. The model explains about 13.35 % of the variation of annual stock returns with the leverage variable with most of the significant power.     

Jump risk and cross section of stock returns: evidence from China’s stock market

Various studies have confirmed the existence of jumps in different financial markets. However, there is sparse theoretical or empirical effort to examine the dynamic relation between jump risk and cross-sectional expected stock returns. We follow a stylized SDF-based diffusion-jump model to examine its testable implications about the relation between cross-section expected excess returns and variations in jump intensities across stocks. The zero-cost portfolio, exploiting the return spreads between the top and bottom decile portfolios formed on jump intensity, could earn an annualized return as high as 24% with an annualized Sharpe ratio of 1.67. A Fama-MacBeth test shows that stock excess returns monotonically decrease in jump intensity even after controlling for other common risk factors.     

What do movements in financial traders’ net long positions reveal about aggregate stock returns?

Previous financial economics studies have successfully identified the existence of informed trading in futures markets; however, there is no study on the specific type of strategy chosen by informed agents to maximize profits. To fill this gap in the literature, we investigate the importance of movements in futures traders’ net long positions in predicting aggregate equity market returns. This study finds that movements in the net long positions of bond, commodity, and stock futures traders are strong predictors of aggregate stock returns as they outperform a large number of popular return predictors both in and out of sample. In addition, a one-standard-deviation change in futures traders’ net long positions can lead to an increase (decrease) of up to 3.4% (4.12%) in annualized market excess equity returns. The study’s first-order autocorrelation results reveal an absence of persistence in the net long predictors. A vector autoregression decomposition shows that the economic source of financial traders’ net long position predictive power stems predominantly from the discount rate and cash flow channels. Overall, the study finds that financial traders are informed traders who are able to anticipate future aggregate cash flows and associated discount rate news.     

Brokers’ financial network and stock return

Brokerage firms are usually not only known for trading stocks for their retail clients in return for commission fee but also known for being information distributors of their clients’ investment recommenders. However, only a few studies have examined investors’ trading behaviors within a brokerage firm. This study proposes a financial network model in modeling the information diffusion process of investors within brokerage firms and investigates the potential effect of interconnectedness among brokerage firms on stock returns. We find that the centrality of brokerage firms has strong explanatory power to stock returns even if we control for the Fama–French pricing factors and other characteristics of stock.     

Co-determination of capital structure and stock returns—A LISREL approach: An empirical test of Taiwan stock markets

Titman and Wessels (1988) utilize a structural-equations model (LISREL) to find out the latent determinants of capital structure. Maddala and Nimalendran (1996) indicate that the problematic model specification causes the poor results in Titman and Wessels’ research. Chang, Lee, & Lee (2009) apply a Multiple Indicators and Multiple Causes (MIMIC) model to re-examine the same issue as Titman and Wessels did but found more convincing results. We extend Titman and Wessels’ research from using a single-equation approach to a multi-equations approach. In addition to the determinants of firms’ capital structure, those of stock returns are determined simultaneously. Literature indicates that a firm's capital structure may affect its stock returns (Bhandari, 1988), and the reverse is true too (Baker and Wurgler, 2002, Lucas and McDonald, 1990, Welch, 2004). Hence, a firm's determinants of its capital structure and those of its stock returns should be decided simultaneously, rather than independently. By solving the simultaneous equations, we examine the empirical relationship between the two endogenous variables: capital structure and stock returns and find out their common determinants as well. Our results show that stock returns, expected growth, uniqueness, asset structure, profitability, and industry classification are the main factors of capital structure, while the primary determinants of stock returns are leverage, expected growth, profitability, value and liquidity. The level of debt ratios and stock returns are mutually determined by the aforementioned factors and themselves.     

Does bank capitalization matter for bank stock returns?

We examine US bank capitalization and its association with bank stock returns, and find that the book- and market-based capital ratios show different patterns. Fama-MacBeth regressions and portfolio analyses suggest that banks’ market-based capital ratios are negatively associated with banks’ stock returns during the (tranquil) 1994–2007 period while book-based capital ratios are positively associated with banks’ stock returns during the (turbulent) 2008–2014 period. These results suggest that the effect of bank capitalization on bank stock returns depends on the capital measure used and the period considered.     

Retail investors’ trading and stock market liquidity

The paper investigates the relation between retail investors’ participation in trading and aggregate stock market liquidity. The findings show a positive and significant relation between retail investors’ trading and stock market liquidity. Examination of the determinants of retail investors’ trading reveals that, on average, retail investors with more diversified trading activity tend to trade when liquidity is higher, the frequency of their arrival to the market is not affected by the level of liquidity, and retail investors are willing to trade at a lower liquidity level as sellers than as buyers. Moreover, retail investors’ trading does not create price noise at the aggregate market level. Overall, the evidence suggests that retail investors contribute to market quality.     

Interactions between investors’ fear and greed sentiment and Bitcoin prices

This paper examines the relationship between investor fear in the cryptocurrency market and Bitcoin prices by considering the potential effects of the ongoing COVID-19 pandemic during the period of May 5, 2018 and December 10, 2020. The existence of structural changes in the time series for the full sample reveals a non-constant causality between fear sentiment and Bitcoin prices, which leads us to apply a bootstrap rolling window Granger causality test. Our results show that both negative and positive interactions between fear sentiment and Bitcoin prices occur during several subperiods. The nature of these interactions changes significantly before and during the pandemic. Thus, we contribute to the fast-growing literature on the financial effects of the COVID-19 global pandemic, as well as to the debate on whether to classify Bitcoin as a new asset, speculative investment, currency, or safe haven asset.     

United States oil and gas stock returns with multi-factor pricing models: 2008–2018

International oil demand is met by large government-owned producers, with remaining production divided between publicly traded Majors and Independents. This study compares publicly traded oil producer equity returns traded on the NYSE for the Majors and Independent US oil producers. Individual producer returns were related to equity and oil returns. Equity market risk is lower for large Majors and higher for smaller Independent rivals. Oil producers’ risk premiums associated with oil are smaller for large Majors and larger for smaller Independents. Natural gas returns generally do not affect producer returns. Major returns are inversely while smaller Independent returns are positively related to the size effect. Major oil producer returns are positively related to the value effect and negatively related to momentum.     

Connectedness and risk spillovers in China’s stock market: A sectoral analysis

This paper shows how sectors in the Chinese stock market are connected and investigates risk spillovers across these sectors. Using graph theory and a recently developed time series technique, we are able to identify the systemically important sector in the market and the patterns of risk spillovers across sectors over time. Unlike standard econometric modeling, graph theory enables us to approach this question in a more reader-friendly way. The empirical results show that Industrial sector plays a central role and should thus be considered the systemically most important sector in the Chinese stock market. The spillover structure is found to be time-varying. While Industrial sector dominates the system for most of the time, other sectors such as Consumer Discretionary sector also occasionally appear as the central sector. Our empirical results also indicate that the simple correlation-based approach can produce equally useful information as more advanced econometric models.     

A bivariate causality between stock prices and exchange rates: evidence from recent Asianflu☆

This paper applies recently developed unit root and cointegration models to determine the appropriate Granger relations between stock prices and exchange rates using recent Asian flu data. Via impulse response functions, it is found that data from South Korea are in agreement with the traditional approach. That is, exchange rates lead stock prices. On the other hand, data of the Philippines suggest the result expected under the portfolio approach: stock prices lead exchange rates with negative correlation. Data from Hong Kong, Malaysia, Singapore, Thailand, and Taiwan indicate strong feedback relations, whereas that of Indonesia and Japan fail to reveal any recognizable pattern. %JEL classification: F300; G150     

Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?

Solar energy is one of the fastest growing sources of electricity generation. Forecasting solar stock prices is important for investors and venture capitalists interested in the renewable energy sector. This paper uses tree-based machine learning methods to forecast the direction of solar stock prices. The feature set used in prediction includes a selection of well-known technical indicators, silver prices, silver price volatility, and oil price volatility. The solar stock price direction prediction accuracy of random forests, bagging, support vector machines, and extremely randomized trees is much higher than that of logit. For a forecast horizon of between 8 and 20 days, random forests, bagging, support vector machines, and extremely randomized trees achieve a prediction accuracy greater than 85%. Although not as prominent as technical indicators like MA200, WAD, and MA20, oil price volatility and silver price volatility are also important predictors. An investment portfolio trading strategy based on trading signals generated from the extremely randomized trees stock price direction prediction outperforms a simple buy and hold strategy. These results demonstrate the accuracy of using tree-based machine learning methods to forecast the direction of solar stock prices and adds to the broader literature on using machine learning techniques to forecast stock prices.     

Investor co-attention and stock return co-movement: Evidence from China’s A-share stock market

This study attempts to link investor co-attention to stock return co-movement in China's A-share stock market. On the one hand, stock price will co-move for stocks within the same industry and within the same market, which is labelled “return co-movement”. On the other hand, investor attention will also co-move as investors systematically search for relevant information for stocks of similar characteristics or as the stocks experience common information shocks, which is termed “investor co-attention”. The empirical evidence suggests that stock return co-movement can be explained by investor co-attention to a great extent, even after controlling for stock fundamentals and firm characteristics, and this effect is more salient for stocks with lower institutional ownership. Moreover, we employ large national lottery jackpots as exogenous shocks to investor attention. The empirical findings show that the co-movement of both investor attention and stock return increase on large lottery jackpot days, while investor co-attention contributes less to return co-movement on large lottery jackpot days. In summary, we offer an alternative explanation for return co-movement by observing the causal relationship between investor co-attention and stock return co-movement.     

How do stock prices respond to fundamental shocks in the case of the United States? Evidence from NASDAQ and DJIA

In this paper, we use both the Dow Jones and NASDAQ indices to test the robustness of Binswanger's (2004c) finding that US stock market dynamics are governed mostly by nonfundamental shocks or speculative bubbles after the 1982 debt crisis. We estimate a total of 72 SVAR models and 36 SVECM models. We determine that the findings are robust indeed and that fundamental shocks have become less and less important over the years, irrespective of which US stock market index is considered.     

Earnings disaggregation and analysts＇ forecasts

Accounting concepts dictate that separately disclosed components should contain separate useful information. This paper examines the relations between income statement components and analysts＇ earnings forecasts and forecast errors. Regressions explaining earnings forecasts using earnings components provide a better fit than regression using just aggregate income to explain forecasts. We interpret this as consistent with the hypothesis that analysts use incremental information in components not available in aggregate income. However, additional tests based on predictability of forecast errors indicate that analysts do not incorporate all information available in components into earnings forecasts. In addition, this inefficiency appears to increase at longer forecast horizons.     

How does investor attention matter for crude oil prices and returns? Evidence from time-frequency quantile causality analysis

This paper studies the time–frequency, nonlinear quantile relationship between investor attention (GSVI) and crude oil over the period from January 2000 to April 2020. To do so, the wavelet coherency, wavelet-based causality-in-quantiles test and quantile-on-quantile method are employed. The results indicate that first, the correlation between investor attention and crude oil is relatively high, and the highly correlated regions are concentrated from 8 to 16 months. In most cases, the GSVI is negatively correlated with the crude oil market. Additionally, under extreme market conditions, the explanatory ability is stronger than in the normal market, and it is greater in the low-frequency domain than in the high-frequency domain. Finally, investor attention has an apparent asymmetric impact on crude oil prices and returns at each scale, displaying a positive effect on the low quantiles of crude oil but a negative effect on the high quantiles across all quantiles of the GSVI. In the short term, when crude oil prices and returns are in a bear market, the larger volume of the GSVI has a greater impact on them. Moreover, the impact becomes greatest under extreme market conditions.     

How does changing age distribution impact stock prices? A nonparametric approach

This paper examines whether variations in demographic structure have influenced stock prices. The study employs a nonparametric approach based on the Fourier Flexible Form representation, which relates variations in the entire age distribution to the normalized stock price under a flexible functional form. The main findings of this paper are that there is a significant impact from prime working‐age consumers on the stock price, and that this impact is robust for all G5 countries (France, Germany, Japan, the UK and the USA). These findings survive many robust tests, and are consistent with the predictions from the life‐cycle models. Copyright © 2009 John Wiley & Sons, Ltd.     

How does changing age distribution impact stock prices? a nonparametric approach

The original article to which this Erratum refers was published in Journal of Applied Econometrics 2009.     

A comparative goodness-of-fit analysis of distributions of some Lévy processes and Heston model to stock index returns

In this paper, we investigate the goodness-of-fit of three Lévy processes, namely Variance-Gamma (VG), Normal-Inverse Gaussian (NIG) and Generalized Hyperbolic (GH) distributions, and probability distribution of the Heston model to index returns of twenty developed and emerging stock markets. Furthermore, we extend our analysis by applying a Markov regime switching model to identify normal and turbulent periods. Our findings indicate that the probability distribution of the Heston model performs well for emerging markets under full sample estimation and retains goodness of fit for high volatility periods, as it explicitly accounts for the volatility process. On the other hand, the distributions of the Lévy processes, especially the VG and NIG distributions, generally improves upon the fit of the Heston model, particularly for developed markets and low volatility periods. Furthermore, some distributions yield to significantly large test statistics for some countries, even though they fit well to other markets, which suggest that properties of the stock markets are crucial in identifying the best distribution representing empirical returns.     

Incentive to manipulate earnings and its connection to analysts’ forecasts, trading, and corporate governance

We develop a model where insiders?? decision to manipulate earnings is linked both to their stake and to corporate governance. We show how earnings manipulation affects analysts?? forecasts and institutional trading. More precisely, whenever there is ??excessive?? earnings manipulation, we observe less optimistic analysts. Furthermore, institutions exhibit positive feedback trading behavior and appear to ??front-run?? analysts?? errors. Finally, companies with strong corporate governance are less prone to these phenomena, being able to avoid the detrimental effects of insiders?? incentives. We then provide strong empirical evidence to support our model.