Optimal prediction model of cyclical downturns proposed by Neftci (1982) was widely used in application to predict turning points of different economies such as US, UK, Japan, and West Germany. I apply Neftci model to predict the occurrence of bear markets of Taiwan stock market by using exchange rate of NTD, monitoring indicator of economy, and turnover rate of TAIEX as predictors. The results show that turnover rate outperforms the other two predictors, which signaled bear markets almost concurrently on average and the variation of the signal time is the smallest, ranging from 4 months lead to 4 months lag. / Optimal prediction model of cyclical downturns proposed by Neftci (1982) was widely used in application to predict turning points of different economies such as US, UK, Japan, and West Germany. I apply Neftci model to predict the occurrence of bear markets of Taiwan stock market by using exchange rate of NTD, monitoring indicator of economy, and turnover rate of TAIEX as predictors. The results show that turnover rate outperforms the other two predictors, which signaled bear markets almost concurrently on average and the variation of the signal time is the smallest, ranging from 4 months lead to 4 months lag.

The study uses Moving Average, On Balance Volume, and KD (Stochastic Oscillator) to analyze that the technical analysis in which the bull or bear stock markets is efficiency. Also, verifies the changes of market efficiency before and after the financial crisis and whether it can earn excess returns or not by using technical analysis. That is, the returns earned by using technical analysis significantly greater than buy and hold which means the efficiency of technical analysis. Nevertheless, the study also aims to realize that whether the returns of the portfolio of technical indicators better than unit indicator. The companies in our samples are selected by the size of market value top 30 companies in the industries of electronic and finance in order to avoid the effect of market micro structure. Our results are as follows: (1) The returns in bear market are significantly higher than bull market by using MA6-144. (2) The MA6-72 and MA6-144 of financial stock before financial crisis, the returns of technical analysis are significantly better than buy and hold. In the other hand, in the electronic stock, we can use MA6-22-250, KD, and OBV to beat the buy and hold strategy and verify that the market efficiency does not exist. (3) The returns which combine of KD and OBV indicators are significantly higher than KD.

Baltijos šalių akcijų rinkų istorija yra trumpa, tuo tarpu akcijų kainų ciklų nagrinėjimui bei galimam vėlesniam prognozavimui reikalinga ilgesnė duomenų imtis. Dėl šios priežasties šiame darbe bus siekiama išsiaiškinti ar šių šalių akcijų rinkos elgesys yra panašus į Skandinavijos šalių akcijų rinkų pokyčius. Panašumams ar skirtumams atskleisti teorinėje dalyje buvo nagrinėjami akcijų rinkos cikliškumo charakteristikos, o taip pat ciklų atsiradimo ir jų koreliacijos sąlygos. Antrojoje dalyje šalių ekonominės situacijos nagrinėjimui pasirinkti tokie rodikliai kaip: infliacijos ir BVP dinamika. O akcijų ciklai buvo identifikuojami pagal modifikuotą NBER metodą. Darbe buvo iškeltos trys hipotezės, kurios buvo nagrinėjamos tre�����iojoje darbo dalyje. Panašumas buvo nagrinėjamas trimis aspektais, kurie yra iškeltų hipotezių pagrindas: akcijų rinkos indekso charakteristikos, indeksų pokyčių koreliacija, ekonominių sąlygų akcijų kainų indeksų pakilimo metu panašumas. Patvirtintos buvo tik antroji ir iš dalies trečioji hipotezės. Vertinant bendrai tris hipotezes, Skandinavijos šalių patirtis netinka Baltijos šalių akcijų rinkos tendencijoms prognozuoti, nes egzistuoja daug skirtumų. / The history of stock markets in Baltic states is short, whereas analysis of stock market cycles requires longer time series data. Therefore, the objective of this thesis is to find out whether the behavior of stock market in above mentioned countries is similar to Scandinavian stock market changes. In theoretical chapter of paper work characteristics and conditions of stock market cycles were examined to determine similarities or differences in analyzed countries. Second chapter studies economic background and identifies stock market cycles using NBER method. Three hypotheses were raised based on three aspects of stock market cycles: stock market cycle characteristics, correlations of stock indices’ changes and economic background in light of stock cycles’ peaks. Only second and third hypotheses were proved. Assessing all three hypotheses Scandinavian stock market history is not suitable to make prognosis for stock markets cycles in Baltic states because of lots of differences.

In this study, we address two research questions: 1) Can we identify bear market episodes in Australia in the past 20 years? 2) How do investors’ moods change as stock market conditions enter into a bear phase. To address the first question, we use a pattern recognition algorithm, called the penalised LSE approach. By defining bear markets as those stock market regimes where the average returns are statistically significantly negative or below the risk free rate, we are able to detect two bear market periods in Australia in the past 20 years. These are the November 1987 to February 1988 and the April 2000 to May 2000 periods. To address the second question, we study the change in investors’ attitudes to varieties of systematic risk and the aggregate number and dollar value of shares traded in portfolios as a result of the regime switch from pre-bear to bear period. Out of the 7 categories of risk considered in this study, the transition from pre-bear to bear regime in both sample periods had a significant impact mainly on investors’ attitude toward the size risk factor. Investors systematically became more sensitive to firm size as stock market conditions entered into the 1987⁄1988 bear market. In the later sample period, investors’ reaction to firm size was more selective as it depended on the characteristics of the stocks that made up their portfolios. We also find that the regime switches resulted in lower portfolio trading volumes. Based on these results we infer that the November 1987-February 1988 bear market evoked a general sad mood, while the April 2000-May 2000 bear market stirred up both angry and sad feelings in market participants depending on the composition of stocks in their portfolios.

This thesis extends the 52-week high momentum literature, which was first published by George and Hwang in 2004, by stressing the parameters of the trading strategy to investigate its robustness. George and Hwang, in their seminal paper, find that the ratio of a stock’s close price to its 52-week high price is a good predictor of future returns. The thesis stresses various parameters of the strategy - such as the percent of total stocks bought and sold each period – and applies the strategy over different time periods – such as bull and bear markets. The study finds that the strategy is more profitable over the later half of the data set due to underperformance in bear markets such as the 1929 market crash and subsequent Great Depression. The results also show a significant difference in profitability between bull and bear market periods. The second half of the thesis looks at a new area in momentum, the absolute 52-week high. The strategy buys stocks whose price has increased over the previous six months, and who also close to their 52-week high price. Stocks are only bought (sold) if their price has increased (decreased) over the past six months and is close to (far from) the 52-week high price. The aim is to cut out stocks that are considered to be underperforming in the 52-week high momentum strategy, leaving only true winner and loser stocks. This strategy was found to increase the strength of the 52-week high momentum strategy, and the results show that there is no longer a significant difference between bull and bear market returns.

In questa tesi, l’analisi verte su risk-adjusted performance, proprietà statistiche e caratteristiche dei fondi hedge (FH). Nel primo articolo, i risultati relativi al survivorship bias e backfill bias indicano che l’impatto delle distorsioni è diverso a seconda delle strategie. Utilizzando il modello multifattoriale di Fung and Hsieh (2004), l’analisi della performance indica che il 42% dei FH ha ottenuto un rendimento superiore al mercato. Infine, utilizzando dei metodi parametrici e non parametrici, l’analisi della persistenza indica differenti livelli di persistenza a seconda della strategia. Nel secondo articolo, vengono analizzati i fondi di fondi hedge (FOHFs). I risultati sono particolarmente interessanti. In primo luogo, i FOHFs e le sotto strategie hanno generato un excess return positivo; inoltre l’alfa ottenuto attraverso il modello a 7 fattori di Fung and Hsieh (2004) risulta elevato. In secondo luogo, i FOHFs e le sotto strategie hanno un rendimento inferiore a quello dell’indice dei FH. In terzo luogo, le correlazioni tra gli indici dei FOHFs e l’indice azionario sono inferiori rispetto alle correlazioni tra l’indice dei FH e gli indici azionari. Infine, l’indice dei FH e quelli dei FOHFs sono positivamente correlati con l’indice azionario quando il mercato tende al ribasso, ma risultano non correlati con l’indice azionario quando il mercato tende al rialzo. Rispetto all’indice dei FH, gli indici dei FOHFs hanno una correlazione minore con gli indici azionari in entrambe le fasi del mercato, suggerendo che i FOHFs forniscono benefici maggiori in termini di diversificazione rispetto ai fondi hedge puri. / In this thesis, I examine the risk-adjusted performance, statistical properties and fund characteristics of hedge fund investments. In Essay One, results of survivorship bias and backfill bias by investment styles indicate that biases are different across styles. Using a multi-factor model of Fung and Hsieh (2004), the analysis of performance indicates that 42% of the hedge funds significantly outperformed the market. Finally, using parametric and non-parametric methods, the analysis of persistence indicates different degree of persistence depending on the hedge fund strategy. In Essay Two, I analyse fund of hedge funds (FOHFs). I find several interesting results. First, FOHFs and the sub-strategies earn positive excess returns and a high Fung and Hsieh 7-factor alpha. Second, FOHFs and the sub-strategies underperform the hedge fund index (HFI). Third, the correlations between FOHF indices and equity index are lower than correlations between HFI and equity indices. Finally, hedge funds and FOHFs are positively correlated with the equity index in the bear markets but uncorrelated with the equity index in the bull markets. Compared to HFI, FOHF indices have lower correlation with equity index in both bull and bear markets, indicating that FOHFs provide better diversification benefits than individual hedge funds.

Investors constantly seek information that provides an edge over the market. One of the conventional methods is to find factors which can predict asset returns. In this study we improve the Fama and French Five-Factor model with Regime-Switches, student's t distributions and copula dependencies. We also add price momentum as a sixth factor and add a one-day lag to the factors. The Regime-Switches are obtained from a Hidden Markov Model with conditional Student's t distributions. For the return process we use factor data as input, Student's t distributed residuals, and Student's t copula dependencies. To fit the copulas, we develop a novel approach based on the Expectation-Maximisation algorithm. The results are promising as the quantiles for most of the portfolios show a good fit to the theoretical quantiles. Using a sophisticated Stochastic Programming model, we back-test the predictive power over a 26 year period out-of-sample. Furthermore we analyse the performance of different factors during different market regimes.