The objective of this study is to develop a market sentiment model for financial markets using machine learning, and to illustrate these methods using commodity price data. A market sentiment model may capture the fundamental and crowd psychology of the market, through a variable that uses positive and negative words and phrases. The commodity price used is the daily price of the spot crude oil exchange-traded fund (ETF), United States Oil Fund (USO). The forecasting power of the market sentiment model is compared with a traditional autoregressive model. The results showed that the autoregressive models did not have significant forecasting power for the oil data over the time period examined and the addition of the sentiment model did not improve the forecasting power. Machine learning is a relatively new forecasting method. Therefore, further research on this topic is needed before any firm conclusions can be drawn regarding the effectiveness of this approach. / February 2017

This thesis merges the fields of Heterogeneous Agent Models (HAMs) and Be- havioural Finance in order to bridge the main deficiencies of both approaches and to examine whether they can complement one another. Our approach suggests an alternative tool for examining HAM price dynamics and brings an original way of dealing with problematic empirical validation. First, we present the original model and discuss various extensions and attempts at empirical estimation. Next, we develop a unique benchmark dataset, covering five par- ticularly turbulent U.S. stock market periods, and reveal an interesting pattern in this data. The main body applies a numerical analysis of the HAM extended with the selected Behavioural Finance findings: herding, overconfidence, and market sentiment. Using Wolfram Mathematica we perform Monte Carlo sim- ulations of a developed algorithm. We show that the selected findings can be well modelled via the HAM and that they extend the original HAM consider- ably. Various HAM modifications lead to significantly different results and HAM is also able to partially replicate price behaviour during turbulent stock market periods. Bibliographic Record Kukačka, J. (2011): Behavioural Breaks in the Heterogeneous Agent Model. Master thesis, Charles University in Prague, Faculty of Social Sciences,...

This thesis presents three essays on the main determinants and regulations of international capital flows. The essays contribute to an ongoing significant debate among scholars and practitioners on what determines international capital flows by examining the following issues: Global liquidity, market sentiment and financial stability indices; Global liquidity and capital flow regulations; and Global governance and gross capital flows dynamics. In the first essay, we explore the main determinants of global liquidity, measured using cross-border claims of banks, and establish the link between a variety of financial stability indices and global liquidity. For a sample of 149 countries between 2000 and 2016, we find that Bloomberg Financial Stability Indices are more powerful in explaining global liquidity than FRED Financial Stress Indices and the Euro Area Systemic Stress Composite Indicator (CISS). Moreover, both market sentiment indices, namely the US Conference Board Leading Economic Index (LEI) and the US IBD/TIPP Economic Optimism Index are economically and statistically significant on cross-border bank flows. The research provides useful insights on what market sentiment and financial stability indices are better to employ for financial markets surveillance and as such practice of investment management. We argue that anyone interested in using financial stability indices as indicators of financial conditions and the level of financial stress would benefit from tracking several indices and not just one. The second essay examines the effectiveness of capital controls and macroprudential policies as ways to manage the volume of international capital flows, controlling for other determinants. The findings show that capital controls imposed on inflows generally prevail over controls imposed on outflows in reducing the magnitude of capital flows. The results are consistent with the pecking order theory on capital flows and are connected with the riskiness of different asset classes. For a sample of 112 countries over 2000 and 2016, we find that FX and/or countercyclical reserve (RR_REV) and general countercyclical capital buffer requirements (CTC), reserve requirement ratios (RR) and concentration limits (CONC) are the most effective macroprudential policies for managing countries' exposures to global liquidity fluctuations. Moreover, progress is being made to reduce the systemic risks created by systemically important financial institutions (SIFIs) using macroprudential policies. The results reflect recent developments in Basel III regulations and shed light on the effective calibration of capital flow regulations to country-specific circumstances. The final essay examines the link between global governance indicators and patterns of gross capital flows, controlling for other determinants. For a sample of 67 countries between 2000 and 2016, we contribute to explain the existence of the Lucas paradox (1990) on "why doesn't capital flow from rich to poor countries" and the Feldstein-Horioka puzzle (1980). The findings show that institutional quality rather than the effect of diminishing returns of capital is a key explanation for the Lucas paradox. Finally, we provide new evidence on the relationship between the multidimensional nature of financial development and gross capital flows. The findings show the importance and predominance of financial institutions versus financial markets in the dissemination of international capital flows across counties.

La rationalité parfaite des investisseurs, base de l'hypothèse d'efficience desmarchés, est de plus en plus discutée. Ceci a conduit au développement de la financecomportementale. Le sentiment de marché, qui en est issu, est l'objet de cette étude.Après l'avoir mis en relation avec la rationalité et défini, ses modes de mesure courantset une évaluation de leur capacité à anticiper les rentabilités sont présentés. Ensuite, autravers de deux recherches largement indépendantes, nous (1) montrons de manièreempirique, essentiellement à partir de modèles multi-Agents et d'une modélisation del'impact des chocs d'information sur la distribution des rentabilités, que les skewness etkurtosis de la distribution des rentabilités peuvent être utilisés comme indicateurs dusentiment de marché ; (2) mettons en évidence la présence de mémoire sur de nombreuxindicateurs de sentiment, ce qui invalide les modalités habituelles de leur utilisation,dans le cadre de stratégies contrarian. / The perfect rationality of investors, one of the foundations of theefficient market hypothesis, is increasingly being questioned. This has led to thedevelopment of behavioral finance. Market sentiment, which stems from it, is the focusof this study. Having first linked this concept to rationality and defined it, this studygoes on to present the most common ways of measuring market sentiment and assesstheir ability to anticipate market returns. Then, using two different studies, we do twothings (1) using mainly multi-Agent models and by modeling the impact of informationshocks on the distribution of returns, we empirically show how skewness and kurtosis inthe distribution of returns can be used as market sentiment indicators; (2) wedemonstrate that many standard sentiment indicators are processes affected by long- orshort-Term memory, making them invalid as contrarian indicators even though this ishow they are typically used.

This thesis examines the effect of coronavirus-related sentiment on Swedish stock market returns during the coronavirus pandemic. We study returns on the large cap and small cap price indices OMXSLCPI and OMXSSCPI during the period January 2, 2020 – April 30, 2020. Coronavirus sentiment proxies are constructed from news articles clustered into topics using latent Dirichlet allocation and scored through sentiment analysis. The impact of the sentiment proxies on the stock indices is then measured using a dynamic multiple regression model. The results show that the proxies representing fundamental changes in our model — Swedish Politics and Economic Policy — have a strongly significant impact on the returns of both indices, which is consistent with financial theory. We also find that sentiment proxies Sport and Coronavirus Spread are statistically significant and impact Swedish stock prices. This implies that coronavirus-related news influenced market sentiment in Sweden during the research period and could be exploited to uncover arbitrage. Finally, the amount of sentiment-inducing news published daily is shown to have an impact on stock price volatility. / Denna studie undersöker den effekt coronavirus-relaterat sentiment haft på avkastningen på svenska aktieindex under coronaviruspandemin. Vi studerar avkastningen på large cap- och small cap-prisindexen OMXSLCPI och OMXSSCPI under perioden 2 januari 2020 – 30 april 2020. Proxier för coronavirus-sentiment konstrueras från nyhetsartiklar som klustrats i ämnen genom latent Dirichlet-allokering och poängsatts genom sentimentanalys. Sentimentproxiernas påverkan på aktieindexen mäts sedan med en dynamisk multipel regressionsmodell. Resultaten visar att proxierna som representerar fundamentala förändringar i vår modell — svensk politik och ekonomisk policy — har en starkt signifikant inverkan på avkastningen på båda indexen, vilket är konsekvent med finansiell teori. Vi finner även att sentimentproxierna sport och spridning av coronaviruset är statistiskt signifikanta i sin påverkan på svenska aktiepriser. Detta innebär att coronavirus-relaterade nyheter påverkade marknadssentiment i Sverige under undersökningsperioden och skulle kunna användas för att upptäcka arbitrage. Slutligen visas mängden sentimentframkallande nyheter publicerade per dag ha en inverkan på aktieprisvolatilitet.

本文考量波動度不對稱效果(Volatility Asymmetric Effect)與均數回歸(Mean Reverting)兩個特性，並考量台股市場特性，嘗試建立一個適合台灣市場的波動度交易策略。利用GARCH(1,1)波動度與VIX指標建構第一個交易訊號，並建立當日沖銷部位。以賺取日內行情為出發點，利用時間序列模型捕捉波動度的高估或低估且搭配純跨式(Pure Straddle)策略或根據Delta調整後的跨式(Adjusted Straddle)策略。第二個交易訊號則是利用市場敏感指標，觀察外資與自營商在交易部位與未平倉部位的變化，找出對於波動度的影響。建立由選擇權與期貨組成的Delta-Hedged部位，藉由觀察市場上主力籌碼的變化，動態調整部位契約，尋找波段之間的獲利機會。 實証部分以期交所公布的每日交易資料與VIX日資料，利用2007至2008兩年的歷史資料，估計參數與測試交易訊號。樣本外期間為2009年1月開始至3月結束共55個交易日。考量交易成本後，兩個不同型態的交易訊號，仍然能夠藉由本研究的策略，獲得正的報酬。本文認為台灣為一個淺碟市場，過度反應資訊的特性，讓波動度策略出現獲利的機會。藉由這個波動度交易系統的研究，除了讓資金豐沛的機構投資人使用外，也能夠讓一般投資大眾建立自己的波動度交易策略 關鍵字:波動度交易，選擇權交易策略，GARCH(1,1)，VIX，市場情緒指標 / Trying to apply a preliminary study of volatility trading strategies in Taiwan derivative market is the topic of this dissertation. Capturing the market movement or even the dynamic of underlying asset is a Pandora’s Box for academic researchers and industry participants. Mean-reverting and asymmetrical effects are the two special characteristics of volatility for us to design our trading system according to the previous empirical studies. In our study, we use different type of volatility signal to capture the trading opportunities. Use the new released information form TAIFEX including VIX and Position Structure of Institutional Traders to design our signal. We apply the idea to use pure option position and delta-hedged position as our trading tools in this volatility trading system and look for the opportunities between realized volatility and implied volatility. An over-reaction may rises the uncertainty and also lead the market volatility change coherently. We use history data from 2007 to 2008 test our trading signal and parameters. The out sample period is from 2009 January to 2009 March which has 55 trading days to simulate our strategies. In the end, we see a positive result in both trading signals which earns positive return after considering the trading cost. Key words: Volatility Trading, Market Sentiment Indices, Option Strategies, VIX, GARCH(1,1)