Market Mood Research Articles

A Comprehensive Analysis of the Behavioural Factors Influencing Stock Market Investing

To engage in stock market investment, one must navigate a complex environment that encompasses several aspects such as financial analysis, economic data, and market mood. However, a sometimes-overlooked aspect that plays a crucial role in influencing investment choices is the field of human psychology. The topic of behavioural finance examines the impact of cognitive biases and behavioural tendencies on investors' behaviours, which in turn affects market dynamics. This academic analysis examines eleven prominent behavioural factors, ranging from cognitive dissonance to endowment bias, to clarify how they emerge in investment choices and the resulting impact on stock market performance. By being aware of and recognising these biases, investors can take proactive steps to reduce their influence, thereby creating an environment that promotes making wellinformed and logical investing choices that match with long-term financial goals.

A Study on Futures and Options in Derivative

Examining the processes that influence risk and return profiles for market players, this research probes the complicated world of futures and options markets. This study takes a holistic view of the topic, delving into market behavior studies, quantitative models, and historical data to provide a detailed picture of how financial market options and futures work. Part one of the research delves into the theory of futures and options, explaining how these derivative products work. It delves into the ways these monetary instruments help hedgers manage risk, investors make speculative bets, and arbitrageurs use them strategically. Futures and options are both distinguished from one another in the research, with the study elaborating on the specific features and consequences of each. In the future, the study will examine the volatility patterns and risk-adjusted returns of several asset classes' futures and options markets. The research intends to find patterns and outliers that have affected the risk-reward environment for market players by looking at case studies and past market occurrences. Option pricing models and their use in analyzing market expectations and implied volatility are further explored in the paper. The study investigates the effect of market mood on option pricing and uses empirical analysis to assess how well option pricing models capture market dynamics.

The Impact of the Fed’s Monetary Policy in 2022 on China’s Stock Market: Evidence from SSEC and SZSE

International financial markets have been abuzz with worry over the Fed’s interest rate rise strategy. The Fed’s interest rate raises policy’s implementation will have wide-ranging effects, particularly for an emerging market nation like China. The influence of the Fed’s interest rate hike policy on the Chinese market will be examined in this essay in several different ways, including stock market yields, volatility, economic growth, and market expectations. The results of this study imply that the Chinese stock market has been negatively affected over the long term by the Fed’s interest rate hike. In particular, the returns of the Shanghai Securities Composite Index and Shenzhen Securities Composite Index were significantly negatively impacted by the Fed’s rate hike, with the SZSE index being more adversely impacted. Additionally, this study discovered that the Fed’s interest rate hike had no effect on the SZSE Index’s market volatility throughout the study period but had a considerable impact on the market volatility of the SSEC Index. This implies that a Fed rate hike may result in greater SSEC Index market volatility while having a negligible effect on SSEC Index market volatility. In order to stabilize market mood and reduce risks, the government and authorities should closely monitor any potential effects of the Fed’s interest rate hike on the Chinese stock market. To adjust to the evolving global financial climate, investors should carefully evaluate market risks and develop prudent investment strategies.

Unveiling Stock Market Trends Implementing LSTM and KNN Algorithms

Abstract: The objective of this study is to enhance and assess the efficacy of Long Short Term Memory (LSTM) and k-Nearest Neighbors (KNN) algorithms in the context of predicting stock prices. This study entails the acquisition of historical data from credible sources pertaining to pricing, as well as other significant variables such trade volume and market mood. Subsequently, the gathered data undergoes a process of cleansing, preprocessing, and refining to ensure its compatibility with the training model. Subsequently, we constructed a forecasting model utilizing Long Short-Term Memory (LSTM) and another forecasting model employing K-Nearest Neighbors (KNN).

Integrating Economic Theory, Domain Knowledge, and Social Knowledge into Hybrid Sentiment Models for Predicting Crude Oil Markets

For several decades, sentiment analysis has been considered a key indicator for assessing market mood and predicting future price changes. Accurately predicting commodity markets requires an understanding of fundamental market dynamics such as the interplay between supply and demand, which are not considered in standard affective models. This paper introduces two domain-specific affective models, CrudeBERT and CrudeBERT+, that adapt sentiment analysis to the crude oil market by incorporating economic theory with common knowledge of the mentioned entities and social knowledge extracted from Google Trends. To evaluate the predictive capabilities of these models, comprehensive experiments were conducted using dynamic time warping to identify the model that best approximates WTI crude oil futures price movements. The evaluation included news headlines and crude oil prices between January 2012 and April 2021. The results show that CrudeBERT+ outperformed RavenPack, BERT, FinBERT, and early CrudeBERT models during the 9-year evaluation period and within most of the individual years that were analyzed. The success of the introduced domain-specific affective models demonstrates the potential of integrating economic theory with sentiment analysis and external knowledge sources to improve the predictive power of financial sentiment analysis models. The experiments also confirm that CrudeBERT+ has the potential to provide valuable insights for decision-making in the crude oil market.

Navigating Financial Complexity: Language Processing Insights for Modern Businesses

Complex financial markets generate massive volumes of unstructured text data from news, publications, social media, and analyst comments. NLP can assist investors and traders make real-time judgments using this data. Analysis of markets This article reviews NLP advancements in three main areas. Generally favorable, negative, or neutral opinions toward companies, industries, or markets. Conversational tone predicts market and investor confidence using machine learning and sentiment lexicons. Finance conversation topic discovery. NLP algorithms may find hidden patterns and investment possibilities by identifying frequently occurring keywords, phrases, and entities in vast text corpora. Classifying critical events like profits, mergers, and regulations. NLP canwarn traders and improve real-time trading decisions by gathering vital news and financial data.Sentiment analysis and transaction volume are used in stock market case studies. Market mood may affect trading performance, since numerical models link favorable sentiment to stock price increases. The importance of active trading in price changes is shown by regular trade volumes

Functions of Artificial Intelligence, Income Investment Instrument, and Crypto Money in Era of The Fourth Revolution (ITALIC)

This research examines those related to Artificial Intelligence, stock robotics, and revenue intruments portfolio diversity in the context of the Fourth Industrial Revolution era, artificial intelligence, and concern for the environment. First, our results suggest that a portfolio of these assets is highly dependent, suggesting that large mutual losses are likely to occur during times of disruption in the economy. Second, it supports a large market mood in the near future, indicating that this disruption in the approaching time can increase the market mood of assets, while to support the market mood decreases for a long time.

Functions of Artificial Intelligence, Income Investment Instrument, and Crypto Money in Era of The Fourth Revolution

This research examines those related to Artificial Intelligence, stock robotics, and revenue intruments portfolio diversity in the context of the Fourth Industrial Revolution era, artificial intelligence, and concern for the environment. First, our results suggest that a portfolio of these assets is highly dependent, suggesting that large mutual losses are likely to occur during times of disruption in the economy. Second, it supports a large market mood in the near future, indicating that this disruption in the approaching time can increase the market mood of assets, while to support the market mood decreases for a long time.

B3 Stock Price Prediction Using LSTM Neural Networks and Sentiment Analysis

This article presents an approach to predict stock prices which incorporate sentiment analysis from Twitter posts as an input to an Long Short Term Memory (LSTM) Neural Network to help in the decision process. The sentiment analysis measures subjectivity and polarity as well as the number of tweets about the company to capture the market mood, which influences the stock prices, were evaluated. The main company used to evaluate our method is Vale (VALE3). The sentiment analysis helps to reach an Root Mean Squared Error (RMSE) of 0.021. We also validate our method with JHSF (JHSF3) and Usiminas (USIM3), obtaining RMSE of 0.012 and 0.016, respectively.

Cognitive economics and the Market Mind Hypothesis: Exploring the final frontier of economics

AbstractCognitive economics partners cognitive science and economics: cognitive science teaches economics about mentality (present in markets), whereas economics teaches cognitive science about market forces (present in minds). The shared Market Mind principle is intelligent (sometimes conscious) self‐organisation. Specifically, the Market Mind Hypothesis formalises what investors casually refer to as ‘the market's mind’. Prices are its informational signatures while market mood is its phenomenal experience.

On the persistence of market sentiment: A multifractal fluctuation analysis

This paper applies multifractal detrended fluctuation analysis to study the multifractal property and temporal persistence of U.S. and European stock market sentiment, providing deeper insights into investor behavior. The findings indicate that the average sentiment is anti-persistent, understood as a general tendency for investors to overreact. The multifractal spectrum has a significant width in both markets, which comes with a substantial variation in local sentiment persistence. Analyses show that the current sentiment persistence is positively related to the level of market mood. Hence, investor fear is related to overreacting, while optimism is more closely to a random walk. This result is of potential interest for investment strategies, but is also likely to affect the stability of a financial market. Conclusions are drawn from financial option and survey data.

Forward looking up-/down correlations

<abstract> Equity returns are typically higher correlated during market downturns than during bullish times. This paper develops a novel approach how investor expectations for such correlation asymmetries can be quantified from forward-looking data. Based on option implied volatilities, it is found that the correlation asymmetry is significant, rejecting the use of the classic mono-correlation assumption. Further, the spread between expected down and up correlations is time-varying and positively dependent on the current market mood: stock diversification is more difficult when it is needed the most. Thus, the three main advantages of the proposed model are (ⅰ) the distinction between up- and down-correlations, (ⅱ) it actually captures investor expectations as traded in current market prices and (ⅲ) the immediate response to the current market outlook. Practical relevance of this paper is highlighted by the computation of expected up-/down CAPM betas. </abstract>

Fear and Laughing of the Market: Trending Pessimism, Fragile Optimism

Building on fractional Black-Scholes, this paper draws a connection between option implied Hurst exponent H and current market mood. H comes with several advantages over VIX and survey based sentiment measures, such as a straight forward interpretation (optimistic/pessimistic) or the highly frequent data availability. From global evidence, we find strong correlations of H with a broad range of well established sentiment gauges. Analyzing H in more detail, investor fear occurs much faster than confidence is gained back. From periodical plus rolling long-term memory analysis for eight major regions around the globe, we observe that persistence in sentiment is depended on its level: the higher the market mood, the more overreacting and fragile it becomes. Other way around, if pessimism rises, then the mood gets stable and trending.

Forward Looking Up-/Down Correlations

Equity returns are typically higher correlated during market downturns than during bullish times. This paper develops a novel approach how investor expectations for such correlation asymmetries can be quantified from forward-looking data. Based on option implied volatilities, we find that the correlation asymmetry is significant, rejecting the use of the classic mono-correlation assumption. Further, the spread between expected down and up correlations is time-varying and positively dependent on the current market mood: stock diversification is more difficult when it is needed the most. Thus, the three main advantages of the proposed model are (i) the distinction between up- and down-correlations, (ii) it actually captures investor expectations as traded in current market prices and (iii) the immediate response to the current market outlook. Practical relevance of this paper is highlighted by the computation of expected up-/down CAPM betas.

Asymmetric relationship of investor sentiment with stock return and volatility: evidence from India

PurposeThe study examines the cross-sectional and asymmetric relationship of investor sentiment with the stock returns and volatility in India.Design/methodology/approachThe investor sentiment is captured using a market-based measure Market Mood Index (MMI) and a survey-based measure Consumer Sentiment Index (CSI). The asymmetric effect of the relationship is examined using quantile causality approach and cross-sectional effect is examined by considering indices such as the BSE Sensex, and the various size indices such as BSE Large cap, BSE Mid cap and BSE Small cap.FindingsThe result of the study found that investor sentiment (MMI) cause stock returns at extreme quantiles. Lower sentiment induces fear-induced selling, thereby lowers the returns and high sentiment is followed by lower future returns as market reverts to fundamentals. On the other hand, bullish shifts in sentiment lower the volatility. There exists a positive feedback effect of stock return and volatility in the formation of investor sentiment.Originality/valueThe study captures both asymmetric and cross-sectional relationship of investor sentiment and stock market in an emerging economy, India. The study uses a novel data set (i.e.) MMI which captures the sentiment based on market indicators and are widely disseminated to the public.

Business intelligence analytics using sentiment analysis-a survey

Sentiment analysis (SA) is the study and analysis of sentiments, appraisals and impressions by people about entities, person, happening, topics and services. SA uses text analysis techniques and natural language processing methods to locate and extract information from big data. As most of the people are networked themselves through social websites, they use to express their sentiments through these websites.These sentiments are proved fruitful to an individual, business, government for making decisions. The impressions posted on different available sources are being used by organization to know the market mood about the services they are providing. Analyzing huge moods expressed with different features, style have raised challenge for users. This paper focuses on understanding the fundamentals of sentiment analysis, the techniques used for sentiment extraction and analysis. These techniques are then compared for accuracy, advantages and limitations. Based on the accuracy for expexted approach, we may use the suitable technique.

A Model for Collective Emotion Forecasts Financial Data

Collective behaviour sums up the emotional and rational actions of many individuals. In the financial markets, the economic agents’ aggregate activity leads to price fluctuations driven by hope and fear, optimism and pessimism. While the market mood mechanism is unknown, a number of hypotheses exist including the suggestion that the mind’s complex cognitive processes scale up to the domain of socioeconomic activity. Here we show how a neurocomputational model for primitive individual emotion and memory can predict the highest and lowest daily prices of NASDAQ-traded companies. The model, known as the Grossberg–Schmajuk recurrent gated dipole, beats some state-of-the-art econometric tools for emotion–influenced financial data. This finding comprises an indirect evidence for the existence of a fractal projection from individual to collective cognition.

가상화폐의 은유적 개념화 양상

Metaphor is a method of understanding new information through already known information. An article about economy seems very objective as it delivers fact-oriented information, but its conceptualization could differ in accordance with the intention of a writer. Once the writer choses a specific metaphor for information delivery, a reader builds a meaning frame according to the intention of writer and understands the information within the frame. According to the examination on metaphoric expression used in articles about virtual currency, it used conceptual metaphor that makes virtual currency look like dangerous beings. There were conceptual metaphors like VIRTUAL CURRENCY IS MINERAL, VIRTUAL CURRENCY IS A ORGANISM, VIRTUAL CURRENCY IS A MAN, VIRTUAL CURRENCY MARKETING IS WAR. Also, there were metaphoric expressions in accordance with characteristics such as demand and supply, competition, capital transfer and market mood such as ECONOMIC ACTION IS RACE, MONEY IS AN ENTITY, ECONOMIC SITUATION IS THE MOVEMENT OF WATER/AIR.

Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications

This paper investigates the possibility of learning a trading rule looking at the relationship between market indicators and decisions undertaken regarding entering or quitting a position. As means to achieve this objective, we employ a long short-term memory machine, due its capability to relate past and recent events. Our solution is a first step in the direction of building a model-free robot, based on deep learning, able to identify the logic that links the market mood given by technical indicators to the undertaken investment decisions. Although preliminary, experimental results show that the proposed solution is viable and promising.

Financial Risk Tolerance of Individual Equity Investors in Indian Stock Market

Financial markets offer a wide choice of asset class and financial instruments to investors to meet their investment objectives. Investors must consider not only the market mood and the required return but must ascertain the risk involved in committing the funds. Apart from this, they must have an accurate measure of their risk tolerance as risk is pervasive in all financial pursuits. The uncertainty surrounding the financial markets gives birth to risk. Therefore, understanding the financial risk tolerance behaviours of individual equity investors in a fast growing economy like India is vital for policy makers, regulators like SEBI, professional portfolio managers and the individual retail investors. The present study employs Grable and Lytton (published in Financial Services Review in the year 1999) thirteen item instrument for assessing financial risk tolerance of 802 respondents. The two risk tolerance categories, namely; Below Average Risk Tolerance Category and Above Average Risk Tolerance Category vary significantly in their attitude and tolerance towards risk with mean FRT scores of 24.01and 33.08 respectively. Of the demographic variables, gender depicts that females are less tolerant to risk primarily in Below Average FRT category. Risk tolerance is also influences by education level. The lower income groups depict lower tolerance towards financial risk supporting the assertion the low income people get naturally risk averse. Risk tolerance substantially increases with increase in investors’ investment experience. It may be inferred that demographic variables play an important role in deciding the financial risk tolerance levels of individual equity investors in India.