This thesis is an attempt to bridge some research gaps in the area of behavioural finance and investment through adopting the three essays scheme of PhD dissertations. There is a widespread belief that the traditional finance theory failed to provide a sufficient and plausible explanation for (1) what motivates individual investors to trade, (2) the pattern of their trading and the formation of their portfolios, (3) the determinants of cross section of expected returns other than risk. Behavioural Finance, however, offers more realistic assumptions based on two building blocks; behavioural biases of irrational investors and the limits of arbitrage that prevent the arbitrageurs from correcting mispricing and pushing prices back to fundamental values. This dissertation is structured as follows: In the first essay, the disposition effect is defined as the propensity of investors to realize gains too early while being loath to realize losses. Capital gains overhang is a measure of unrealized capital gains and losses that is associated with the disposition effect and the trading activities of behaviourally biased investors. We discover that firm characteristics can play a role in explaining variations in the capital gains overhang that is consistent with the activities of behaviourally biased and disposition investors. Specifically, we find that capital gains overhang is increasing in firm attributes that attract behaviourally biased investors, namely, earnings per share, leverage, growth and size. Capital gains overhang is also declining in market liquidity, possibly because liquidity allows behaviourally biased investors to excessively trade shares and beta and corporate earnings, probably because when high risk and inefficient firms experience losses, disposition investors experience capital losses that they are reluctant to realize. In the second essay, quantile regressions are employed to analyse the relationship between the unrealized capital gains overhang and expected returns. The ability of the disposition effect to generate momentum is also considered for the extreme expected return regions (0.05th) and (0.95th) quantiles. To do so, 450,617 observations belonging to 5176 US firms are employed, covering a time span from January 1998 to June 2015. Following the methodology of Grinblatt and Han (2005), the findings show significant differences across various quantiles in terms of signs and magnitudes. These findings indicate a nonlinear relationship between capital gains overhang and expected returns since the impact of capital gains overhang as a proxy for disposition effect on expected returns vary across the expected return distribution. More precisely, the coefficients of capital gains overhang are significantly positive and decline as the expected returns quantiles increase from the lowest to the median expected return quantiles. However, they become significantly negative and rise with the increase in expected returns quantiles above median expected returns quantiles. The findings also suggest that the disposition effect is not a good noisy proxy for momentum at the lowest expected return quantile (0.05th). However, interestingly it seems to generate contrarian in returns at the highest expected returns quantile (0.95th). In the third essays, we try to discover systematic disagreements in momentum, asymmetric volatility and the idiosyncratic risk momentum return relationship between high-tech stocks and low-tech stocks. We develop several hypotheses that suggest greater momentum profits, fainter asymmetric volatility and weaker idiosyncratic risk-momentum return relation in the high-tech stocks relative to the low tech stocks. To this end, we divide 5795 stocks that are listed in the Russell 3000 index from January 1995 to December 2015 into two samples SIC code and analysed them using the Fama French with GJR-GARCH-M term. The results show that the high-tech stocks provide greater momentum profits especially for portfolios that have holding and ranking periods of less than 12 months. In most cases momentum returns in the high-tech stocks explain a symmetric response to good and bad news while the momentum returns in the low-tech stocks show an asymmetric response. Finally, the idiosyncratic risk-momentum return relation is insignificant for high-tech stocks while it is significant and negative for low-tech stocks. That is, as idiosyncratic risk increases, momentum decreases for low-tech stocks. These findings are robust to different momentum strategies and to different breakpoints.

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第一篇論述討論處置效果於選擇權市場的實證。處置效果係指投資人在處分資產時，傾向盡快賣出有未實現利得的投資部位，並且繼續持有有未實現損失的投資部位的行為偏誤現象。文獻上有關處置效果的實證多半集中在股票市場而少有於選擇權市場的實證。選擇權市場一般認為是具有私有資訊及較具備金融知識與經驗的投資人會選擇交易的市場。本文實證處置效果在指數選擇權市場上的影響。我們認為對於選擇權投資人來說，價內外程度是最重要且顯而易見的資訊，是很直觀可以衡量可能利得及損失的參考點。相較於傳統衡量根據過去交易價格所形成的未實現損益指標，價內外程度更能吸引投資人的注意力。以本文所提出的基於價內外程度衡量之賣出傾向指標(Moneyness-based Propensity to Sell, MPS)以及根據Grinblatt and Han (2005)所形成的調整後未實現資本利得指標(adjusted Capital Gains Overhang, ACGO)，每周將買權(賣權)排序成五等分後，我們發現持有最高等分的MPS或ACGO的買權(賣權)並賣出最低等分的買權(賣權)所形成的投資組合能夠產生超額報酬，顯示處置效果在指數選擇權市場亦存在。利用雙重排序(double sorting)的方法，我們發現MPS相較於ACGO，是較能夠在選擇權市場捕捉處置效果的指標。第二篇論述討論相似度衡量策略在期貨市場獲利的可能性。文獻上對於技術交易是否能產生顯著的報酬結果並不一致，然而實務上分析過去的價格走勢並使用技術指標所產生的訊號，是廣泛被接受的。現有測試技術交易指標獲利能力的文獻，通常假設投資人在實證測試的樣本期間一致性的參考某個交易指標產生的交易訊號並依此交易。然而實務上投資人可能同時參考不同的交易指標，每次交易可能根據不同交易指標所產生的訊號，且投資人會從歷史交易價格走勢中尋找類似於現有走勢的狀況，以這些歷史走勢接續的報酬率做為現有走勢未來報酬率的預期值。本文中我們提出一個較符合實際狀況的決策過程來描述技術交易投資人的行為，並重新檢視技術交易的獲利能力。我們提出的決策過程包含三個步驟。首先投資人建立一個特徵向量，包含投資人所認為足以預測未來報酬率並足以描述現況的指標。第二個步驟，投資人從過去某段期間中尋找相似於現有特徵向量的歷史狀況，並以這些歷史狀況接續的報酬率來作為預測的根據。最後，投資人依照過去的歷史狀況與現在有多相似，作為接續報酬率的加權權重，並以相似度權重加權平均報酬來做為未來報酬率的預測值，我們將依照相似度加權報酬所產生交易訊號所形成的策略稱為相似度衡量交易策略(Similarity-based trading rules)。我們檢視相似度衡量交易策略在九個不同的期貨市場中的獲利能力，在考量data-snooping及交易成本後，每日相似度衡量交易策略仍在其中六個市場中獲得顯著的報酬率。 / The disposition effect, which refers to the tendency of investors to selling their winning investments too soon and to hold losing investments too long, has been well-documented in the extant literature. However, while empirical researches focus on examining the behavioral bias in the stock market, little attention is paid to the option market, where most informed investors and sophisticated traders gather. This essay tests for the disposition effect on the index options market. We argue that moneyness, the most salient and readily available information for option investors, is a natural reference point for potential gains and losses, which likely attracts market participants’ attention more than traditional measures that are based on past trading prices. Based on the Moneyness-based Propensity to Sell (MPS) measure that we introduce and an adjusted capital gains overhang (ACGO) measure of Grinblatt and Han (2005), we find that a strategy formed by buying calls/puts in the highest MPS or ACGO quintile and selling those in the lowest quintile would generate significant abnormal returns, suggesting the presence of the disposition effect. Using double sorting method, we find that the MPS is better as a measure in capturing the disposition effect on the options market than the ACGO. While the literature documents mixed results for the profitability of technical trading rules, the use of technical buy/sell signals based on analyzing past prices is widely accepted by practitioners. The existing literature on testing the predictive ability of technical trading mostly assumes that a technical investor consistently makes investment decisions based on the buy/sell signals according to one particular trading rule during the entire sample period. However this may be far from reality. Technical investors may simultaneously make predictions based on different technical indicators and follow different technical signals. Furthermore, they analyze historical price patterns that are similar to the current market condition and make assessment of future returns based on the subsequent returns of these similar patterns. The process is known as charting. We attempt to propose a more realistic decision-making process that incorporates the similarity-based predictors to account for technical investors’ decisions in the real world and reexamine the profitability of technical trading rules. The proposed process includes three steps. First, the investor attempts to predict future returns based on a vector of current characteristics that is sufficient for his assessment of the future returns and to depict the present scenario of the stock market. Second, the investor searches for the similar patterns in a specific time window prior to the current date and make an assessment of the future returns based on how similar these past patterns and the current pattern are and how rewarding the subsequent returns of the similar patterns are. Third, the investor is assumed to form a similarity-based indicator which is an assessment of the future returns depended on the similarity-weighted average of all previously observed values of the subsequent returns. The technical investor is then assumed to buy/sell according to the signals generated by the similarity-based trading rules (SBTR). We examine the profitability of the SBTR in nine futures markets and find significantly positive and robust returns after considering the data-snooping adjustments and transaction costs in six of the nine markets.