A Model for Stock Price Fluctuations Based on Information

The author presents a new model for stock price fluctuations based on a concept of information. In contrast, the usual Black-Scholes-Merton-Samuelson (1965, 1973) model is based on the explicit assumption that information is uniformly held by everyone and plays no role in stock prices. The new model is based on the evident nonuniformity of information in the market and the evident time delay until new information becomes generally known. A second contribution of the paper is to present some problems with explicit solutions which are of value in obtaining insights. Several problems of mathematical interest are compared in order to better understand which optimal stopping problems have explicit solution

A distribution benefits model for improved information on worldwide crop production. Volume 1: Model structure and application to wheat

The improved model is suitable for the study of benefits of worldwide information on a variety of crops. Application to the previously studied case of worldwide wheat production shows that about $108 million per year of distribution benefits to the United States would be achieved by a satellite-based wheat information system meeting the goals of LACIE. The model also indicates that improved information alone will not change world stock levels unless production itself is stabilized. The United States benefits mentioned above are associated with the reduction of price fluctuations within the year and the more effective use of international trade to balance supply and demand. Price fluctuations from year to year would be reduced only if production variability were itself reduced

Real-Time Stock Trend Prediction via Sentiment Analysis of News Article

The stock market is volatile and volatility occurs in clusters, price fluctuations based on sentiment and news reports are common. A trader uses a wide variety of publicly available information to forecast the marketing decision. This paper proposes an advice to traders for stock trading using sentimental analysis of publically available news reports. It is based on a hypothesis, that news articles have an impact on the stock market, with this hypothesis we study the relationship between news and stock trend and also proved that negative news has a persistent effect on the stock market. In order to prove this assumption semi-supervised learning technique is being used to build the final model of news classification. This research shows that SVM with TF-IDF as feature performs well in further analysis. The accuracy of the prediction model is more than 90% having 52% correlation with the return label of a stock. This paper also proposes a real-time system which fetches news of any company on a real-time basis and displays its top five news and also predicts the adjusted close price of the next seven days. Keywords: Text Mining, Human Sentiments, KNN, Random Forest, Multinomial Naïve Bayes, linear SVM, News

Relationship between oil price and sector index returns: Evidence from Nordic & Qatari markets

The purpose of this paper is to investigate the relationship between oil price changes and stock market returns. The paper examines how oil price fluctuations influence on the returns of industry-level indices in Nordic and Qatari markets. The purpose for studying both markets is the aim of being able to compare sector-level oil price correlations between the stock markets of oil-importers and oil-exporters. By comparing the oil price correlations between the Nordic and Qatari markets, it is possible to find out the possible market-specific relationships with oil as a commodity. The study investigates the relationship between unexpected oil price changes and sector index returns by examining the seven available sector indices from Qatar Stock Exchange and 24 Nasdaq OMX Nordic sector indices. The examined oil price index is West Texas Intermediate Cushing. The indices are analyzed with both weekly and monthly returns for the period from April 2012 to September 2017. The study utilizes a standard market model that is expanded with the oil price factor in order to estimate the sector-level correlation coefficients for oil price sensitivity. In addition, the paper examines if the oil price sensitivity is asymmetric or not. The asymmetric model is included with a dummy variable to capture the correlations for both positive and negative unexpected oil price changes. This paper contributes empirical findings to the study of Nandha and Faff (2008). The contribution of this paper is presenting more focused and detailed information of the relationship between oil price changes and market-specific industry-level stock indices. Based on the main findings of this paper, the oil price sensitivities are both sector- specific and market-specific. In contrast to previous studies, this paper presents empirical evidence that oil price correlations are mostly positive across industries in both Nordic and Qatari stock markets.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Essays on Market Microstructure

This dissertation studies topics on market microstructure. The first chapter theoretically studies market manipulation in stock markets in a linear equilibrium. The second chapter empirically examines the presence of opportunities for liquidity arbitrage. The last chapter develops and examines a method to capture a co-movement of informed trading. In chapter 1, I study a theory of trade-based price manipulation in markets. I compare two different types of price manipulation studied in previous literature, uninformed and informed manipulation, in the same linear equilibrium model. I show that the presence of positive-feedback traders creates an incentive for the informed trader to bluff, but the opportunity is absent if a sufficient number of uninformed traders behave strategically. Numerical comparable statics results show that informed manipulation is more likely and more profitable when the noise trading is more volatile and that market efficiency could become worse under the presence of manipulation. A financial transaction tax can not prevent informed manipulation, but it reduces the liquidity of the market. Chapter 2 empirically investigates intra-day price manipulation in a stock market. My microstructure model is specifically designed to define the conditions under which a manipulation opportunity arises from the variation in liquidity as measured by price impact. My empirical analysis using data from the Tokyo Stock Exchange suggests that while there is a significant chance of uninformed manipulation across time and stock codes, it is not profitable enough to affect price fluctuations. Analysis of intraday price and trade sizes shows that the opportunity begins to disappear shortly. Chapter 3 studies contagion in a financial market by using a market microstructure model. We extend the Easley, Kiefer, and O'Hara (1997) model to a multiple-asset framework. The model allows us to identify whether the driving forces of informed trading common or idiosyncratic information events are. We apply the method to three groups of stocks listed on the New York Stock Exchange: American Depositary Receipts (ADRs) of developed and emerging countries, and blue chips. We find contagion among emerging-country ADRs during the Asian Financial Crisis of 1997, in the sense that informed trades were mostly driven by common information events

Predicting stock market movements using network science: An information theoretic approach

A stock market is considered as one of the highly complex systems, which consists of many components whose prices move up and down without having a clear pattern. The complex nature of a stock market challenges us on making a reliable prediction of its future movements. In this paper, we aim at building a new method to forecast the future movements of Standard & Poor's 500 Index (S&P 500) by constructing time-series complex networks of S&P 500 underlying companies by connecting them with links whose weights are given by the mutual information of 60-minute price movements of the pairs of the companies with the consecutive 5,340 minutes price records. We showed that the changes in the strength distributions of the networks provide an important information on the network's future movements. We built several metrics using the strength distributions and network measurements such as centrality, and we combined the best two predictors by performing a linear combination. We found that the combined predictor and the changes in S&P 500 show a quadratic relationship, and it allows us to predict the amplitude of the one step future change in S&P 500. The result showed significant fluctuations in S&P 500 Index when the combined predictor was high. In terms of making the actual index predictions, we built ARIMA models. We found that adding the network measurements into the ARIMA models improves the model accuracy. These findings are useful for financial market policy makers as an indicator based on which they can interfere with the markets before the markets make a drastic change, and for quantitative investors to improve their forecasting models.Comment: 13 pages, 7 figures, 3 table

Value Relevance of Accounting Information in Chinese Listed Companies: An empirical study based on Ohlson valuation framework

This paper examines empirically how investors perceive the information content of accounting information under Chinese Accounting Standards for explaining stock prices over time. The study investigates the value relevance of accounting information in Chinese listed companies for the period from 2000 to 2015 based on Ohlson’s (1995) valuation framework. The results demonstrate that earnings and book value are value relevant to the pricing process in all years. Furthermore, the combined value relevance of earnings and book value does not have a temporal trend of either increase or decrease, but fluctuates substantially over time. The relative importance of earnings and book value in explaining stock price also fluctuates remarkably and exceeds each other alternately over time. Besides, negative earnings and large intangibles do not change the trend of fluctuations in the value relevance of earnings and book value, and the fluctuations are not correlated with the stock market sentiment. In addition, this paper develops Ohlson’s valuation framework by adding financial leverage and operating size as independent variables into the previous price model. Results suggest that financial leverage variable has little value relevance and thus should not be included in the extended valuation model. In contrast, operating size has much value relevance incremental to the original price model, particularly in the years when value relevance of earnings and book value becomes relatively lower. The results also suggest that a firm with a larger operating size has disadvantages in the pricing process of stock shares

Oil Price Volatility and the All-share Index: Evidence from Nigeria

Oil price is one of the most important economic factors directing  the world economy. This is even more pronounced for the Nigerian economy that  relies heavily on crude oil export revenues. A change in oil price  is expected to impact on all her economic frontiers. Thus, the aim of this study is to investigate the effect of global  oil price changes on All share  index of  the Nigerian Stock exchange. Relevant data for the estimation were obtained from Central Bank of Nigeria’s data  base ranging from the period January, 2006  to December,2015, making for an equal 120 observations. The end of month  values of the All-share index , which is value weighted constitutes all trading securities on the stock market, These were related with the prevailing  price of oil at the global market for the corresponding periods. A combination of preliminary analysis, short run and long run models were generated in this investigation. The paper established  that,  while the  trace and  max Eigen value tests indicates a case of no co-integration,  the causality test  reveals that none of the variables granger cause each other. The  impulse–response  function  indicates  that , on  the average, All Share Index was feeding on itself .  Variance Decomposition analysis  shows that  at a 10 year horizon,  97.36%  of the variance  in All Share Index are explained by their own shocks. While the VAR estimates suggest  that All share index responds to fluctuations in the global pricing of oil overtime; the ARCH model of order 1 indicates the presence of volatility clustering  on the All share Index. On the other hand,  coefficients of ARCH , GARCH  and ASSYMETRIC GARCH terms did not satisfy the decision rules but  probability of the GARCH in Mean Term did,  suggesting that oil price volatility affects returns on All share index of the Nigeria stock exchange at 1% Alpha level.  Based on the findings of study, we conclude that investors  purposing to invest in the Nigerian stock market should do so with some bit of caution seeing that outcome of some of the  tests  were at variance with each other. They did not all  provide, a satisfactory and predictive information on the trading position of the  Nigerian All share index. This simply infers that, to some extent, the  global price of oil should  be taken cognizance of while predicting the price of stocks in the Nigerian stock market. It is expedient we state here that, the efficiency of capital markets are measured  by the ability of securities to reflect  and incorporate all relevant information  almost instantaneously , in their prices. In other words, how responsive the Nigerian Stock market is to information, will determine the  rate at which  volatility in the global pricing  of crude oil will  affect the pricing of stock and by extension  value of the All share index. Capital markets have been found  to be fairly  efficient   in the advanced economies as well as in a number of emerging capital markets. We therefore recommend  that the Nigerian Securities and Exchange Commission, should strive to make  the Nigerian capital market as efficient as possible Keywords: Nigerian Stock Market , All-share Index, Efficient market, Crude oil price,  Oil price volatility

Three essays on the time series of returns

This dissertation consists of three essays on the time series of asset returns. The first essay in Chapter 1--Time-Varying Drivers of Stock Prices--provides novel evidence of the time-varying roles of subjective expectations in explaining stock price variations across the market and 30 industry portfolios monthly from 1976 to 2020. Cash flow expectations matter more under financial uncertainty and recessions, especially among the hardest-hit industries such as Telecommunications during the Dot-com Bubble, Financials during the Great Recession, and Healthcare during the Covid-19 pandemic. Conversely, discount rates explain more price variations during expansionary periods. Finally, inflation expectations, while accounting for 60 percent of price fluctuations in the high inflationary environment before 2000, play a negligible role thereafter. In the second essay in Chapter 2--Investor Sentiment and Asset Returns: Actions Speak Louder than Words--I analyze daily predictability of investor sentiment across four major asset classes and compares sentiment measures based on news and social media with those based on trade information. For the majority of assets, trade-based sentiment measures outperform their text-based equivalents for both in-sample and out-of-sample predictions. This outperformance is particularly noticeable in long-term forecasts. However, real-time mean-variance investors can only achieve economic gains using Bitcoin trade sentiment, suggesting the challenge of transforming sentiment into daily profitable trading strategies. In the last essay in Chapter 3--War Discourse and Disaster Premia: 160 Years of Evidence from Stock and Bond Markets--using a semi-supervised topic model on 7,000,000 New York Times articles spanning 160 years, I test whether topics of media discourse predict future stock and bond market returns to test rational and behavioral hypotheses about market valuation of disaster risk. Focusing on media discourse addresses the challenge of sample size even when major disasters are rare. Our methodology avoids look-ahead bias and addresses semantic shifts. War discourse positively predicts market returns, with an out-of-sample R2 of 1.35 percent, and negatively predicts returns on short-term government and investment-grade corporate bonds. The predictive power of war discourse increases in more recent time periods.Includes bibliographical references

Consentaneous agent-based and stochastic model of the financial markets

We are looking for the agent-based treatment of the financial markets considering necessity to build bridges between microscopic, agent based, and macroscopic, phenomenological modeling. The acknowledgment that agent-based modeling framework, which may provide qualitative and quantitative understanding of the financial markets, is very ambiguous emphasizes the exceptional value of well defined analytically tractable agent systems. Herding as one of the behavior peculiarities considered in the behavioral finance is the main property of the agent interactions we deal with in this contribution. Looking for the consentaneous agent-based and macroscopic approach we combine two origins of the noise: exogenous one, related to the information flow, and endogenous one, arising form the complex stochastic dynamics of agents. As a result we propose a three state agent-based herding model of the financial markets. From this agent-based model we derive a set of stochastic differential equations, which describes underlying macroscopic dynamics of agent population and log price in the financial markets. The obtained solution is then subjected to the exogenous noise, which shapes instantaneous return fluctuations. We test both Gaussian and q-Gaussian noise as a source of the short term fluctuations. The resulting model of the return in the financial markets with the same set of parameters reproduces empirical probability and spectral densities of absolute return observed in New York, Warsaw and NASDAQ OMX Vilnius Stock Exchanges. Our result confirms the prevalent idea in behavioral finance that herding interactions may be dominant over agent rationality and contribute towards bubble formation.Comment: 17 pages, 6 figures, Gontis V, Kononovicius A (2014) Consentaneous Agent-Based and Stochastic Model of the Financial Markets. PLoS ONE 9(7): e102201. doi: 10.1371/journal.pone.010220