The purpose of the thesis aims to investigate pair trading strategies which are frequently used by hedge funds adopting non-directional strategies. It is also our intention to develop a set of streamlined operational guidelines for pair trading strategy to be implemented in the Taiwan securities markets. Daily closing prices of listed stocks are used. The database is compiled by Taiwan Economic Journal, covering companies listed on the Taiwan Stock Exchange and the GreiTai Market in Taiwan. The company-pairs are selected from firms listed on the same market, conducting business in the same product field, and with sample correlation coefficient higher than 0.7. We choose 10 sample company-pairs covering 20 listed companies. The trading strategies mix both divergence and convergence rules. For the former, when the price ratio of the pair exceeds the moving average price ratio plus (minus) 0.3 standard deviation, we buy the strong and short the weak to anticipate the price ratio trend continues. For the latter, when the price ratio goes beyond the moving average price ratio plus (minus) 1.7 standard deviations, we buy the weak and short the strong, anticipating the price ratio to go back to normal. The exit rules are based on absolute dollar profit, absolute dollar loss, and prolonged position period. The research results show that the pair trading strategies are not risk-free. Risk arises when the price ratio trend runs adversely than as expected. To control the risk, our challenges lie in fine tuning the entry and exit rules. With larger sample size and more in-depth investigation, we expect that the profit/loss ratio of the stragtegy can be improved. Then the pair trading strategy will become a good alternative for conservative individual investors seeking low risk investment opportunities to participate in the securities markets in Taiwan.

This thesis presents two different types of stock trading for a small investor. First described method focuses on stocks from abroad, where with the help of technical analysis a portfolio is constructed. This portfolio is to be traded with the strategy of pair trading or sometimes also called statistical arbitrage. This method is not very well known, so the thesis gives enough space to explain how the system exactly works. Second strategy puts focus on stocks from Prague Stock Exchange and their trading as dividend stocks. Unlike the first strategy, this one presents the fundamental analysis and longterm investment. The emphasis is in both strategies put on easily managable portfolio and its good revenue.

Made available in DSpace on 2016-12-23T14:00:36Z (GMT). No. of bitstreams: 1 Danilo Soares Monte-Mor.pdf: 964390 bytes, checksum: 749870f88ee1c9c692cf782e397379ec (MD5) Previous issue date: 2010-12-17 / Cada vez mais tem surgido no mercado de investimento fundos de retorno absoluto (Hedge Funds) que têm como objetivo principal melhorar seus desempenhos através de estratégias de arbitragem, como é o caso das estratégias long-short. É o comportamento desproporcional e até mesmo antagônico dos preços dos ativos que permite aos players estruturar estratégias para gerar retornos adicionais, superiores aos custos de oportunidade e independentes ao movimento do mercado. Neste trabalho foi utilizada a Teoria de Valores Extremos (TVE), um importante ramo da probabilidade, para que fossem modeladas as séries da relação direta entre preços de dois pares de ativos. Os quantis obtidos a partir de tal modelagem, juntamente com os quantis fornecidos pela normal, foram superpostos aos dados para períodos subsequentes ao período analisado. A partir da comparação desses dados foi criada uma nova estratégia quantitativa long-short de arbitragem, a qual denominamos GEV Long-Short Strategy / Increasingly has appeared on the market of investment Absolute Return Funds (Hedge Funds), which have the main objective to improve their performance through arbitrage strategies, as long-short strategies. It is the disproportionate evolution and even antagonistic of active prices that allows the players to structure strategies to generate additional returns, higher than the opportunity costs and independent of the movement of the market. In this work we used Extreme Value Theory (EVT), an important segment of probability, to model the series of direct relationship between prices of two pairs of assets. The quantiles obtained from such modeling and the quantile provided by normal were superimposed on data for periods subsequent to the period analyzed. From the comparison of such data we created a new quantitative long-short arbitrage strategy, called GEV Long-Short Strategy

Made available in DSpace on 2016-12-23T14:00:36Z (GMT). No. of bitstreams: 1 Danilo Soares Monte-Mor.pdf: 964390 bytes, checksum: 749870f88ee1c9c692cf782e397379ec (MD5) Previous issue date: 2010-12-17 / Increasingly has appeared on the market of investment Absolute Return Funds (Hedge Funds), which have the main objective to improve their performance through arbitrage strategies, as long-short strategies. It is the disproportionate evolution and even antagonistic of active prices that allows the players to structure strategies to generate additional returns, higher than the opportunity costs and independent of the movement of the market. In this work we used Extreme Value Theory (EVT), an important segment of probability, to model the series of direct relationship between prices of two pairs of assets. The quantiles obtained from such modeling and the quantile provided by normal were superimposed on data for periods subsequent to the period analyzed. From the comparison of such data we created a new quantitative long-short arbitrage strategy, called GEV Long-Short Strategy / Cada vez mais tem surgido no mercado de investimento fundos de retorno absoluto (Hedge Funds) que têm como objetivo principal melhorar seus desempenhos através de estratégias de arbitragem, como é o caso das estratégias long-short. É o comportamento desproporcional e até mesmo antagônico dos preços dos ativos que permite aos players estruturar estratégias para gerar retornos adicionais, superiores aos custos de oportunidade e independentes ao movimento do mercado. Neste trabalho foi utilizada a Teoria de Valores Extremos (TVE), um importante ramo da probabilidade, para que fossem modeladas as séries da relação direta entre preços de dois pares de ativos. Os quantis obtidos a partir de tal modelagem, juntamente com os quantis fornecidos pela normal, foram superpostos aos dados para períodos subsequentes ao período analisado. A partir da comparação desses dados foi criada uma nova estratégia quantitativa long-short de arbitragem, a qual denominamos GEV Long-Short Strategy

Pair trading is a well-known strategy based on statistical arbitrage. This strategy uses a short-term deviation from the mean value of the price ratio of two highly correlated stocks from the same sector as the opportunity to open a position. When ratio returns to its mean value again, the position closes. This strategy has been used for many years and the main outcome of this thesis was to test whether this strategy can be profitable even in current market conditions. For that purpose, data ranging from 2010 to April 2017 on all stocks included in the S&P 500 index were used. It was subsequently found that a pair trading strategy generated 25x higher absolute profit in comparison to random agent. Thus, it can still be considered as a profitable strategy.

本文以在中國大陸證券市場上交易量最大，流動性最好的兩隻指數型ETF——華夏上證50ETF（SH510050）和華泰柏瑞滬深300ETF（SH510300）,為一個配對組合，進行統計套利。本文先簡要配對交易的實質和常用方法，以及這一策略目前在全球市場和中國大陸市場上的應用和研究狀況。而後又介紹了這兩隻ETF的標的物——上證50指數和滬深300指數，並闡明為何選取這兩個指數相關的ETF作為統計套利的原因。 接著，分析了華夏上證50ETF和華泰柏瑞滬深300ETF的相關性，從這兩隻ETF的相關性出發，建立共振合模型，並建立一階誤差修正模型對兩隻ETF的短期非均衡狀態進行補充。在此基礎上設定交易規則進行模擬交易。同時我們還在文中後續探討了交易成本和止損點的設置情況。 經過模擬交易，我們發現在一個標準差為開倉閾值的情況下出現的套利機會非常少且收益率較低。因此我們修改交易規則，來探討模型存在的問題，發現當將開倉閾值設為價差序列兩個標準差時，交易次數沒有增加，但收益率有所好轉。當將開倉閾值設為移動平均數和移動標準差，交易次數明顯增加，但收益率並沒有好轉。為進一步驗證上述結論，我們通過樣本外資料進行測試，發現與上述結果一致。此外，我們還通過延長時間序列的方式增加樣本量，得到結果也與上述一致。在用高頻資料交易結果不理想的情況下，我們採用了兩隻ETF的日收盤價格序列建立統計模型和模擬交易，發現在這種情況下，存在套利空間，但第一和第二種策略的套利機會較少，第三種策略套利機會相較前兩種策略要多得多。 分析上述結果產生的原因，主要原因有二：第一，在採用高頻資料的時候，模型的殘差項標準差較小，也就意味著該模型的偏離程度不高，因此套利空間較小。第二，這一配對組合所建立的模型其ECM項係數均非常小，也就意味著模型的長期穩定對時間序列的短期波動影響很小，因此出現的套利機會非常少。 此外，在此說明的是本文所採用的樣本資料為華夏上證50ETF和華泰柏瑞滬深300ETF在2016年7月1日到2016年10月31日每十分鐘的高頻交易價格資料，資料來源為中國大陸的WIND資料庫。 / This essay uses Huaxia SSE50 ETF (Code: SH510050) and Huataiborui CSI300 ETF (Code: SH510300), the two ETFs with the largest trading volume and the best liquidity in the China’s security market, as a pair for statistical arbitrage. Firstly, we introduce the definition of the strategy—pair trading, and its current application in the global and China’s mainland stock market. Then, the essay presents the underlying assets of the two ETFs, SSE50 Index and CSI300 Index, and explains why we choose the two ETFs for statistical arbitrage. Secondly, we analyze the correlation between Huaxia SSE50 ETF and Huataiborui CSI300 ETF, and build the co-integration model based on the correlation. Meanwhile, we establish the first-order error correction model to supplement the short-term imbalance of the two ETFs. On this basis, we set trading rules for simulated transaction. Moreover, we consider trading costs and stop-loss points in this article. After simulated trading, we find that both the trading time and the return are not good enough when we set a standard deviation as the threshold. So we modify trading rules, using the two standard deviations and moving standard deviation as thresholds, but it still doesn’t work. In order to further verify the above conclusion, we change the sample data by adding two times of the original and using the daily closing price, and it reveals that when we use the daily closing price to trade, the yield is better than the high-frequency trading price. There are two reasons for this conclusion. First, the standard deviation of the model’s residual is so little that the arbitrage space is small. Second, the coefficients of ECM is too little, which means the long-term stability of the model has little effect on the short-term volatility of the time series, thus leading to fewer arbitrage chances. In addition, the data used in the article are from the Wind Database in China.

The most useful feature used in finance of the Ornstein-Uhlenbeck (OU) stochastic process is its mean-reverting property: the OU process tends to drift towards its long- term mean (its equilibrium state) over time. This important feature makes the OU process arguably the most popular statistical model for developing best pair-trading strategies. However, optimal strategies depend crucially on the first passage time (FPT) of the OU process to a suitably chosen boundary and its probability density is not analytically available in general. Even for crossing a simple constant boundary, the FPT of the OU process would lead to crossing a square root boundary by a Brownian motion process whose FPT density involves the complicated parabolic cylinder function. To overcome the limitations of the existing methods, we propose a novel class of non-linear boundaries for obtaining optimal decision thresholds. We prove the existence and uniqueness of the maximizer of our decision rules. We also derive simple formulas for some FPT moments without analytical expressions of its density functions. We conduct some Monte Carlo simulations and analyze several pairs of stocks including Coca-Cola and Pepsi, Target and Walmart, Chevron and Exxon Mobil. The results demonstrate that our method outperforms the existing procedures.