Noise Traders Research Articles

Insider trading in discrete time Kyle games

AbstractWe present a new discrete time version of Kyle’s (Econometrica 53(6):1315–1335, 1985) classic model of insider trading, formulated as a generalised extensive form game. The model has three kinds of traders: an insider, random noise traders, and a market maker. The insider aims to exploit her informational advantage and maximise expected profits while the market maker observes the total order flow and sets prices accordingly. First, we show how the multi-period model with finitely many pure strategies can be reduced to a (static) social system in the sense of Debreu (Proc Natl Acad Sci 38(10):886–893, 1952) and prove the existence of a sequential Kyle equilibrium, following Kreps and Wilson (Econometrica 50(4):863–894, 1982). This works for any probability distribution with finite support of the noise trader’s demand and the true value, and for any finite information flow of the insider. In contrast to Kyle (1985) with normal distributions, equilibria exist in general only in mixed strategies and not in pure strategies. In the single-period model we establish bounds for the insider’s strategy in equilibrium. Finally, we prove the existence of an equilibrium for the game with a continuum of actions, by considering an approximating sequence of games with finitely many actions. Because of the lack of compactness of the set of measurable price functions, standard infinite-dimensional fixed point theorems are not applicable.

Insider trading at a random deadline with correlation between dynamic asset and stochastic liquidity

We propose a generalized continuous-time insider trading model, building upon the frameworks of Caldentey and Stacchetti (2010) and Collin-Dufresne and Fos (2016), with a correlation between the value of a risky asset following an Ornstein-Uhlenbeck-type process and the noise trading volume with volatility characterized by a general stochastic process. And a closed form of the market equilibrium is established, consisting of the insider's trading strategy and the market makers' pricing rule. It shows that at the equilibrium: (i) all of the insider's private information is released at the end of the transaction; (ii) market depth and market liquidity evolve as semi-martingales, respectively; and (iii) the equilibrium price is driven by a bridge process that solves an Ornstein-Uhlenbeck-type SDE. Numerical simulations show that as the correlation coefficient increases, the equilibrium price becomes more informative, leading to a decrease in both the trading intensity and the expected payoff for the insider.

Noise trader impact: Bitcoin market evidence from Telegram and X

Noise trader impact: Bitcoin market evidence from Telegram and X

Information acquisition, market professional and discretionary liquidity trading

We analyze an insider trading strategy and market professionals’ information acquisition strategies, and explore their impact on market liquidity and price efficiency in endogenous noise trading. Our findings highlight the negative precision effect on market professionals’ information acquisition. With an increase in market professionals, two opposing effects emerge on market liquidity and price efficiency: the strategic complementarity effect and the precision effect. However, the negative precision effect predominates, which impairs market liquidity and price efficiency. Furthermore, enhancing the ex-ante precision of the risky asset or adjusting liquidity compensation reduces market professionals’ involvement in information acquisition, leading to diminished price efficiency.

Does data asset disclosure contribute to the market efficiency? Evidence from China

Information regarding firms' data assets and stock pricing has been more closely related when data is integrated into firms' value creation processes. However, since data asset disclosure can also be engaged in impression management by firms, its contribution to market efficiency may be impeded. Using a sample of Chinese A-share listed firms from 2014 to 2020, this study examines the impact of increased data asset information disclosure on the subsequent stock price volatility. The findings suggest that more data asset disclosure reduces stock return idiosyncratic volatility. The mechanism analysis shows that data asset disclosure reduces idiosyncratic volatility of stock prices by mitigating analyst forecast dispersion and noise trading induced by buy-sell imbalances. Moreover, this mitigating effect is more pronounced in firms with more analyst following, observable digital investments, and higher liquidity. But the effect is diminished if firms use a more positive abnormal tone in their annual reports.

Economic Behavioral Anomalies In Cryptocurrency Transactions During The Covid-19 Pandemic

The COVID-19 pandemic has had a significant impact on various aspects of life, including financial markets. The cryptocurrency market, already renowned for its volatility, experienced a surge in activity and significant changes in investor behavior during this period. This research aims to analyze various economic behavioral anomalies that emerged in cryptocurrency transactions during the COVID-19 pandemic. This research uses a literature study method to identify several dominant behavioral anomalies, such as FOMO (Fear of Missing Out), Herding Behavior, Noise Trading, Overconfidence, and Anchoring Bias. These behavioral anomalies trigger extreme market volatility, asset bubbles, and financial losses for investors. This research highlights the importance of investor education, market regulation, and technology development to minimize the impact of behavioral anomalies and protect investors in the cryptocurrency market.

News or noise? ESG disclosure and stock price synchronicity

Environmental, social, and governance (ESG), as an important mechanism to promote the sustainable development of the capital market, is bound to have a significant impact on its stock price synchronicity. This paper examines the impact and mechanism of corporate ESG disclosure on stock price synchronicity using data from Chinese A-share listed companies from 2013 to 2022. It analyzes the dual perspectives of information efficiency and noise trading. The findings support the noise trading perspective, as ESG disclosure significantly reduces the impact of noise on stock prices and enhances firms' stock price synchronicity, and the conclusions still hold after a series of endogeneity and robustness tests. It is also found that the positive impact of ESG disclosure on stock price synchronicity is mainly realized through the paths of reducing accrual earnings management and increasing analyst attention. Moreover, the positive impact of ESG disclosure on stock price synchronicity is more significant in the group of non-state-owned enterprises, higher quality of internal control, non-polluting industries, and less market-oriented enterprises. The study in this paper not only enriches the related literature in the field of ESG disclosure and stock price synchronicity but also reaffirms the noise trading theory of the irrational school.

Futures Leads the Spot but Why not so When Market in Shocks? A Time-Varying Price Discovery of Indian Precious Metals

The study empirically measures the average and time-varying contribution of the spot and futures market to the price discovery of benchmark Indian precious metals from June 2005 to March 2023. The two most popular precious metals in India, i.e., Gold and Silver, were selected for the study. The average contribution is measured through VECM (Vector Error Correction Model) and CFW (common factor weights). Additionally, for getting time-varying contribution, VECM is reformulated into state space form and Kalman filter and smoothening are applied for computing time-varying CFW. Findings reveal that price discovery depicts time-varying behaviour during the study period. While most of the time, futures lead the spot, in case of any shocking news, the spot market dominates the futures as noise trading happens in the futures market for both precious metals, i.e., Gold and Silver. Furthermore, futures market volume, volatility, and COVID-19 negatively affect the price discovery in both precious metals.

Climate change and commercial property markets

AbstractThe economic effect of climate hazard events varies by time and by location. This paper investigates how climate shocks to local property markets transmit to capital markets and provides evidence of the extent to which forward‐looking climate risk is capitalized into the public valuations of those property markets. We first quantify the exposure of real estate portfolios to locations that recently experienced climate events (Event Exposure). Using an event study framework, we find that, in the post‐event period, a one‐standard‐deviation increase in ex‐ante Event Exposure is associated with a 0.2–1.4 percentage points decrease in quarterly stock returns. Cross‐sectional analyses reveal that differences in return effects can be explained by variation in the extent to which the area focuses on climate change. Similarly, we find that forward‐looking climate risk assessment negatively affects firm valuations only in markets with a focus on climate change. Consistent with these findings, we provide evidence that climate events (shocks) induce retail investors (noise traders) to decrease their stock holdings and that blockholders tend to take the opposite side in these transactions. We also show that conditioning on consumer sentiment helps to explain cross‐sectional variation in the response of stock returns to climate events.

Firm-Level Investor Sentiment and Risk: Insight from Behavioral Finance

The aim of this study to analyses the investor sentiment, attentions and types of risk which are incurred after specific event like scam etc. This study used monthly dataset of prices from 2006 to 2022 due to availability of data. The event approach is used for data analysis with different data segregation like affected or unaffected datasets of firms. Firm-specific sentiment and attention increased slightly after the scam, indicating market interest and better firm perceptions. Political, noise trader, arbitrage, sovereign, and realised volatility increased during events. The perception of instability and uncertainty suggests scams increase market fears and risk aversion while attracting attention. A detailed comparison between scammed and unaffected firms showed stark differences. Financial scams lowered market confidence and firm stability, causing negative sentiment, lower attention, and higher risks across multiple dimensions. The differential impact suggests that scams' reputational and operational damage can cripple a firm's finances and investor perceptions. The study affects financial economics and market behaviour theory. The study shows how firm-specific sentiment and attention affect risk measures, explaining market dynamics' psychological and behavioural underpinnings. Financial anomalies like scams affect market outcomes due to investor sentiment and attention. Sentiment can force through risk perceptions, so integrated financial analysis models must account for behavioural and psychological factors. Financial scandals affect firm-specific and market-wide variables, according to event studies. With the insights, investors, regulators, and corporate managers can improve market and regulatory practices. This study examines how financial scams affect firm-specific and market-wide variables using theory and practice during and after event.

Retail investors, COVID and the quality of the New Zealand Stock Exchange

In this article, we investigate the impact of the influx of retail investors during the COVID-19 lockdowns on the New Zealand Stock Exchange on the efficiency of the market. On one hand, these investors bring added liquidity to the market, which generally improves market efficiency, and on the other hand, many of these investors were inexperienced retail traders who are often viewed as noise traders harming market efficiency. This natural experiment allows us to investigate the impact of retail investors. Using a sample of 99 New Zealand Exchange listed companies, we collect intraday price and quote data from 1 January 2019 to 24 March 2021. We compute a range of market quality measures across three categories: trading costs, price efficiency and speed of information incorporation. Our results clearly indicate that the influx of retail investors has reduced market quality; in particular, we observe more price predictability and information being impounded into prices more slowly. JEL Classification: C10, G14, G20

Exploring the relationship between Chinese crude oil futures market efficiency and market micro characteristics

This paper uses daily frequency data spanning from March 26, 2018 to February 3, 2023, and combines the exponential generalized autoregressive conditional heteroskedasticity in mean (EGARCH-M) model with the time-varying parameters state-space (TVPSS) model to dynamically test the efficiency of the Chinese crude oil futures market (CCOFM). Additionally, the paper examines the relationship between market efficiency and market micro characteristics such as market depth, market liquidity, noise trading, speculation rate, and information asymmetry. The results show that the CCOFM exhibited weak-form efficiency in June 2020, which lasted until the end of February 2022, and after that, the efficiency showed a poor development trend. Analyzed from the macro level, the Market Maker System and Trader Suitability System have a positive effect on market efficiency, while the Russian-Ukrainian conflict has a negative effect on market efficiency. Analyzed from the micro level, market depth and information asymmetry have a positive effect on market efficiency, while the speculation rate has a negative effect on market efficiency, and noise trading and market liquidity have no significant effect on market efficiency. Market efficiency has a positive effect on market depth and a negative effect on speculation rate. Market liquidity and noise trading have a positive effect on speculation rate, while both information asymmetry and market liquidity have a negative effect on noise trading. In addition, speculation rate has a weak negative effect on information asymmetry, and market depth and speculation rate have a weak positive effect on market liquidity. This study provides theoretical support and decision-making foundation for the development of CCOFM by dynamically exploring the relationship between CCOFM efficiency and micro characteristics.

Post-COVID-19 technology adoption and noise trading: elucidation of investors' sentiments across cultures

PurposeDespite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.Design/methodology/approachDrawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.FindingsThe empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.Originality/valueThis study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

Volume and stock returns in the Chinese market

We propose and empirically investigate hypotheses regarding the volume-return relationship in the Chinese stock market, taking into account the impact of prevalent noise trading and short-selling restrictions. Our findings reveal a negative correlation between trading volume and returns specifically among overvalued stocks. However, no significant correlation is observed for undervalued stocks, nor is there a consistent volume amplification effect. In contrast to our findings, Han, Huang, Huang, and Zhou (2021) also reported a positive correlation between expected return and trading volume for undervalued stocks, and they highlighted the amplified impact of trading volume in the US market. Furthermore, we observe that stricter short-selling restrictions in the Chinese market for overvalued stocks result in a larger deviation from fundamental value as the trading volume increases accompanied by greater disagreement among noise investors. These findings suggest that comprehending market disparities necessitates considering noise trading and short-selling restrictions as crucial factors.

ESG rating disagreement and idiosyncratic return volatility: Evidence from China

Following the increasing importance of sustainable development and the popularity of ESG investing activities, ESG ratings have grown to be crucial references for investors’ decision-making. However, there are substantial disagreements among different rating agencies. This study examines the impact of ESG rating disagreement on idiosyncratic return volatility using data from five prominent rating agencies: SynTao Green Finance, Huazheng, Hexun, Bloomberg, and Rankins ESG Ratings. The findings suggest that ESG rating disagreement will increase idiosyncratic return volatility. This relation is driven by investor attention and noise trading. Heterogeneity tests reveal that the higher analyst coverage and greater analyst forecast bias, the more pronounced the impact of ESG rating disagreement on idiosyncratic return volatility. While firms with foreign investors and more institutional investors can alleviate the interference.

Listening to the noise: On price efficiency with dynamic trading

This paper shows that, in the canonical dynamic rational expectations equilibrium model, public information about future noise trading is potentially detrimental to contemporaneous price efficiency. Our result supports concerns that social sentiment investing, sparked by growing availability of big data and advances in the way of processing it, exacerbates, rather than ameliorates, the negative impact of noise trading on price efficiency.

Tug of war with noise traders? Evidence from the G7 stock markets

This paper studies the tug of war between overnight noise trading and daytime arbitrage in the G7 stock markets. We confirm Akbas, Boehmer, Jiang, and Koch (2022) reporting that this tug of war predicts future returns in the US stock market. We verify this result also in the Canadian stock market. In contrast, for the stock markets of France, Germany, Italy, the UK, and Japan, this tug of war is not predictive for future returns. These country-wise findings are documented in average raw returns and prevail upon risk adjustment along both Carhart (1997) four factors and Fama and French (2018) six factors. A microstructure perspective on the tug of war suggests that the split evidence between the US and Canadian stock markets and the remaining G7 stock markets is due to institutional and regulatory differences which restrict daytime institutional arbitrage in the European stock markets and Japan.

Why does price deviate from net asset value? The case of Singaporean infrastructure REITs

This paper examines the determinants of the changes in premiums to Net Asset Value (NAV) in infrastructure REIT (infra-REIT) share prices in Singapore over the 2017–2021 period. Samples of 11 listed Singaporean REITs (S-REITs) are selected to create a balanced panel data for the analysis. Our finding shows that infrastructure S-REITs have generally been trading at premiums to their NAVs. The first part of the analysis shows that REIT premiums are positively related to REIT size (market cap) and percentage of institutional ownership. On the other hand, REIT premiums are negatively related to dividend yield. Volatility, volume, ROE and liquidity are found to be insignificant in explaining price and NAV divergence. The second part of the analysis finds that there is a higher proportion of uninformed (noise) traders when infrastructure S-REIT prices are diverging from their NAV. This finding supports the Noise theory which implies that there is some evidence that noise traders are present in the infrastructure S-REIT market and their actions might cause irrational departure of REIT prices from the underlying NAV.

Asymmetric information correlation in financial markets

This paper investigates a market model in which the exclusive information of the insider is correlated with the information of outsiders. Our findings suggest that increasing the degree of information correlation can improve market quality, enhancing market liquidity, market efficiency, and total trading volume. Additionally, the degree of information correlation increases the profits of outsiders and noise traders but reducing the profit of the insider. These results indicate that higher degree of information correlation is beneficial to market health, especially during period of economic crisis. Therefore, we suggest that the government and regulatory institutions expand the scope of information disclosure and ensure the accuracy of information disclosed. Furthermore, the study reveals that positive correlation leads to increased trading intensity for outsiders and decreased trading intensity for the insider. In contrast, negative correlation results in more complex changes in trading intensity for both groups due to belief inconsistency and intricate psychological reactions of traders to variations in information. Additionally, we provide an empirical analysis based on data from the Chinese market to support our findings.

Mispricing and Algorithm Trading

This study relaxes the efficient market hypothesis by introducing a model that accounts for initial mispricing and explores the effects of algorithmic trading. The research finds that algorithmic strategies can cause significant market volatility and affect financial stability, particularly when they amplify overpricing, leading to bubbles and crashes. Key insights include: Initial mispricing is crucial for algorithmic trading to impact market prices. Market reactions vary with the direction of the trading strategy relative to the asset’s true value. Informed traders can benefit from mispricing, whereas noise traders typically incur losses. Policy implications suggest that algorithmic trading is not universally harmful; its effects depend on the alignment of trading strategies with accurate pricing. The study advises regulators to differentiate between stabilizing and destabilizing trading practices. For traders, the research highlights the importance of adaptive strategies that help correct mispricing to ensure long-term profitability and market health. This research advances our understanding of algorithmic trading’s dual potential and informs the development of more nuanced financial regulations and trading strategies.