Under contemporary market conditions in China, the stock index has been volatile and highly reflect trends in the coronavirus pandemic, but rare scientific research has been conducted to model the nonlinear relations between the two variables. Added, on the advent that covid-related news in one time period impacts the stock market in another period, time delay can be an equally good predictor of the stock index but rarely investigated. This study utilizes high-frequency data from January 2020 to the first week of July 2022 to model the nonlinear relationship between the stock index, new covid cases and time delay under polynomial regression environment. The empirical results show that time delay and new covid cases, when modelled in a polynomial environment with optimal degree and delay, do present better representation (up to 16-fold) of the nonlinear relationship such predictors have with stock index for China. The representative delay model is used to project for up to 17 weeks for future trends in the stock index. From the findings, the prowess of the time delay polynomial regression is heavily dependent on instability in covid-related time trends and that researchers and decision-makers should consider modeling to cover for the unsteadiness in coronavirus cases.

Investor sentiment has a crucial impact on stock market pricing. Based on prospect theory and partial least squares, we innovatively construct an investor sentiment indicator and verify the validity of the indicator. Compared with other sentiment indices, our investor sentiment index is more effective in in-sample and out-of-sample forecasting. At the same time, from a cross-sectional perspective, both the portfolio analysis and the Fama-Macbeth regression show that the partial least squares results are a better indicator of returns than other indices. The driving force of the sentiment index we construct comes from investors’ perceptions of forecast cash ffow, discount rate, and volatility.

This paper proposes the two-component realized EGARCH model with dynamic jump intensity (hereafter REGARCH-C-DJI model) to model and forecast stock market volatility. The key feature of our REGARCH-C-DJI model is its ability to exploit the high-frequency information as well as to capture the long memory volatility and jump dynamics. An empirical application to Shanghai Stock Exchange Composite (SSEC) index data shows the presence of high persistence of volatility and dynamic jumps in China’s stock market. More importantly, the REGARCH-C-DJI model dominates the GARCH, EGARCH, REGARCH and REGARCH-C models in terms of out-of-sample forecast performance. Our findings highlight the importance of accommodating the realized volatility, volatility components and jump dynamics in forecasting stock market volatility.

In forecast surveys of aggregate macroeconomic and financial variables, the correlation between forecast errors and forecast revisions is positive at the consensus level, but negative at the individual level. Past literature has interpreted this discrepancy as evidence of underreaction to news at the aggregate level and overreaction at the individual level. In this paper, I challenge this view by arguing that noise in predictive judgment can account for the difference. Using a stylized model, I examine how introducing judgment noise at the individual level changes the interpretation of the correlation coefficients. First, a negative coefficient at the individual level no longer necessarily means overreaction. Second, the coefficient at the consensus level underestimates the degree of underreaction. Using forecast survey data, I provide evidence that judgment noise is large enough to reconcile the difference between the two coefficients. The structural parameter measuring over-/underreaction mainly points to underreaction, regardless of whether the model matches correlation coefficients at the individual or aggregate level.

How do expectation biases causally affect households’ financial decisions? We exploit a unique setting and study the repayment decision in solar loans, in which households borrow to purchase and install solar photovoltaic (PV) systems. Electricity production – the benefit that solar panels generate – primarily depends on sunshine duration. This creates exogenous within-person across-period variation in recent signals that borrowers observe and thereby expectations of future electricity production. We find that a one-standard-deviation decrease in sunshine duration in the week right before the repayment date leads to a 20.8% increase of delinquency, even though deviated past sunshine duration does not predict that in the future. Survey evidence shows that agents make more positive forecasts of future electricity production after experiencing longer sunshine duration in the past week. We examine a battery of alternative explanations and rule out mechanisms based on liquidity constraints and wealth effects.

We develop a machine learning (ML) approach to establish new insights into how memory affects ffnancial market participants’ belief formation processes in the field. Using analyst forecasts as proxies for market beliefs, we extract analysts’ mental contexts and recalls that shape forecasts by training an ML memory model. First, we find that long-term memories are salient in analysts’ recalls. However, compared to an ML benchmark trained to fit realized earnings, analysts pay more attention to distant episodes in regular times but less during crisis times, leading to recall distortions and therefore forecast errors. Second, we decompose analysts’ mental contexts and show that they are mainly shaped by past earnings and forecasting decisions instead of current firm fundamentals as indicated by the ML benchmark. This difference in contexts further explains the recall distortion. Third, our comprehensive memory model reveals the significance of specific memory features and channels in analysts’ belief formation, including the temporal contiguity effect and selective forgetting.

We investigate the employment consequences of earnings management. Using audit firm consolidation as an exogenous shock impacting earnings management, we find a positive casual effect of firm-level earnings management on employment growth. The effect is concentrated in privately owned enterprises and firms with higher operational risk, consistent with earnings management affecting labor dynamics by influencing employees’ perceptions of job security and subsequent career decisions. We further document a crowding out effect in local labor market, where a firm’s earnings management negatively influences the employment growth of local peer firms.

This study examines the relationship between analyst forecast dispersion or accuracy and supplier concentration of listed firms in China from 2008 to 2019. Our findings suggest that higher supplier concentration is associated with lower analyst forecast dispersion, which can be attributed to the increased attention it receives from analysts. Moreover, this effect is more pronounced when firms have less bargaining power and higher institutional ownership, indicating a greater reliance on the supply chain. Our study highlights the importance of disclosing supply chain information, which provides insight beyond traditional financial information.

This study examines whether sell-side analysts have the ability to disseminate information consistent with anomaly prescriptions in China. I adopt 192 trading-based and accounting-based anomaly signals to identify undervalued and overvalued stocks. Analysts tend to give more (less) favorable recommendations and earnings forecasts to undervalued (overvalued) stocks. On analyzing the information content, I find that analyst recommendations and earnings forecasts are consistent with accounting-based information rather than trading-based information. Analysts make recommendations and earnings forecasts consistent with anomalies, especially when firms experience relatively bad firm-level information. Additionally, undervalued (overvalued) stocks are associated with high (low) analyst coverage. The results indicate that analysts may contribute to mitigating anomaly mispricing and improving market efficiency in China.

This paper proposes a new forecasting framework for the stock market that combines machine learning algorithms with several technical analyses. The paper considers three different algorithms: the Random Forests (RF), the Gradient-boosted Trees (GBT), and the Deep Neural Networks (DNN), and performs forecasting tasks and statistical arbitrage strategies. The portfolio weight optimization strategy is also proposed to capture the model's return and risk information from output probabilities. The paper then uses the stock data in the Chinese A-share market from January 1, 2011, to December 31, 2020, and observes that all three machine learning models achieve significant returns in the Chinese stock market. The DNN achieves an average daily return of 0.78% before transaction costs, outperforming the 0.58% of the RF and 0.48% of the GBT, far exceeding the general market level. The performance of the weighted portfolio based on the ESG score is also improved in all three machine learning strategies compared to the equally weighted portfolio. These results help bridge the gap between academic research and professional investments and offer practical implications for financial asset pricing modelling and corporate investment decisions.