A behavioural model of investor sentiment in limit order markets

Chiarella, Carl; He, Xue-Zhong; Shi, Lei; Wei, Lijian

doi:10.1080/14697688.2016.1184756

Cited by 18 publications

(11 citation statements)

References 30 publications

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“…Hung [47] finds that investor sentiment significantly affects the order submission behavior in the market and that investors become more active in optimistic times. Chiarella et al [48] find that investor sentiment has an effect on order submission.…”

Section: E Relationship Between Investor Sentiment and Stockmentioning

confidence: 99%

The Effect of Online Investor Sentiment on Stock Movements: An LSTM Approach

Wang

2020

Complexity

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With more and more investors exerting their voices through network forums or social media platforms, the relationships between online investor sentiment and stock movements have drawn more and more attention. In this paper, we crawl stock comments from China’s most popular online stock forum, East Money (www.eastmoney.com), and then develop a sentiment classifier using the LSTM method. Using the online investor sentiment of the stock forum, we explore the effect of online investor sentiment on the stock movements of CSI300. The results show that online investor sentiment has a significant positive impact on both stock return and trading volume and remains significant after controlling book-to-market ratio, BETA, and market value. Moreover, investor sentiment has a significant positive impact on order imbalance of big trade, which represents the main flow of money in the market. As a result, investor sentiment has a positive impact on the major fund flows in the market. In other words, an increase in investor sentiment can boost the major money flows in the market to some extent. From a practical point of view, investor sentiment can assist investors to make investment decisions and help the government to regulate the stock market.

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Section: E Relationship Between Investor Sentiment and Stockmentioning

confidence: 99%

The Effect of Online Investor Sentiment on Stock Movements: An LSTM Approach

Wang

2020

Complexity

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“…The majority of the stock market ABMs search to explain stylized facts by the interaction of fundamental and chart traders with heterogeneous expectations and do not explore the role of anchoring, representative and availability heuristics and behavioral biases such as investor sentiment (excess optimism and pessimism) [10]. The formation of heterogeneous expectations by bounded rational agents under conditions of uncertainty implies a relevant role for investor sentiment in the endogenous dynamics of price formation (see [18] and [19]): excess volatility, heavy tails and volatility clustering strongly suggest that self-perpetuating effects or positive feedback loops are at play. So, despite the fact that mathematical models with processes like GARCH or HAWKES explicitly describe such feedback effects, they do not provide an understanding of its microscopic source ([20] [21] and [22]).…”

Section: Introductionmentioning

confidence: 99%

Anchoring Heuristics, Investor Sentiment and Stylized Facts in the Stock Market: An Agent Based Model

Higachi¹,

Faria²,

Sbicca³

et al. 2020

TEL

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The objective of this paper is to contribute to a theoretical explanation based on Behavioral Finance of three stylized facts of stock market actions which are considered puzzles by Efficient Market Hypothesis (EMH): an excess of volatility in relation to fundamentals, heavy tail distributions of returns, and volatility clustering. Using an agent-based model (ABM), this paper examines the dynamics of fluctuations in the rate of return of shares in an artificial financial environment for three simulation scenarios: 1) 100% of fundamental agents, 2) 75% fundamental and 25% chart agents using anchoring heuristics (eight rules of share price forecasts) and 3) the same composition of agents of scenario 2, in which the chart agents suffer from excess of confidence or pessimism in terms of their expectations. The presence of chart agents in scenario 2 is necessary and sufficient to generate and explain the excess of price volatility and the rate of return of shares. In scenario 3, the sentiment of heterogeneous chart agents explains the heavy tail distributions of share returns and volatility clusters. Also, the linear auto-correlation of absolute rates of return decays slowly to become insignificant in large lags, while the log values of the linear auto-correlation function of rates of returns decays quickly to become insignificant in small lags. The model simultaneously shows the emergence of three of the main stylized facts of the stock market, increasing the micro-diversity of chart agents and the realism of the expectation formation rules.

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“…The model presented in this paper differs from previous works in that many details of current intra-day markets are abstracted away to focus on the strategic search for a local pricing equilibrium. As such, we incorporate some form of herding effect and shared sentiment in the fast component of our model, as done in distinct ways in LeBaron and Yamamoto (2008) or Chiarella et al (2017). In order to obtain our distributional results, however, there is no need to include a detailed implementation of a continuous double auction that would most likely reinforce our findings.…”

Section: Introductionmentioning

confidence: 99%

Fast traders and slow price adjustments: an artificial market with strategic interaction and transaction costs

Liuzzi

Pellizzari

Tolotti

2018

J Econ Interact Coord

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In this paper, we propose an artificial market to model high-frequency trading where fast traders use threshold rules strategically to issue orders based on a signal reflecting the level of stochastic liquidity prevailing on the market. A market maker is in charge of adjusting prices (on a fast scale) and of setting closing prices and transaction costs on a daily basis, controlling for the volatility of returns and market activity. We first show that a baseline version of the model with no frictions is able to generate returns endowed with several stylized facts. This achievement suggests that the two time scales used in the model are one (possibly novel) way to obtain realistic market outcomes and that high-frequency trading can amplify liquidity shocks. We then explore whether transaction costs can be used to control excess volatility and improve market quality. While properly implemented taxation schemes may help in reducing volatility, care is needed to avoid excessively curbing activity in the market and intensifying the occurrence of abnormal peaks in returns. Response to Reviewers: Dear Editor, we submit a deeply revised version of our work and thank you for the request to polish once more the text. We decided to use a professional editor and we have indeed fixed many typos and improved the language in (literally) dozens of places. We truly believe the paper is now more readable and clearer. Do not hesitate to contact us if any further action is needed. Best regards,

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A behavioural model of investor sentiment in limit order markets

Cited by 18 publications

References 30 publications

The Effect of Online Investor Sentiment on Stock Movements: An LSTM Approach

The Effect of Online Investor Sentiment on Stock Movements: An LSTM Approach

Anchoring Heuristics, Investor Sentiment and Stylized Facts in the Stock Market: An Agent Based Model

Fast traders and slow price adjustments: an artificial market with strategic interaction and transaction costs

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