This paper uses a back-propagating neural network (BPN) to predict the price movements of major crypto currencies, leveraging technical factors as well as measurements of collective sentiment derived from the micro-blogging network Twitter. Our dataset consists of daily, hourly and minutely price levels for Bitcoin, Ether and Litecoin along with 8 popular technical indicators, as well as all tweets with the currencies' cash tags during respective time periods. Insprired by previous research which suggest that artificial neural networks are superior forecasting models in this setting, we were able to create a system generating automated investment decisions on a daily, hourly and minutely time basis. The study concluded that price trends are indeed predictable, with a correct prediction rate above 50% for all models, and corrensponding profitable trading strategies for all currencies on an hourly basis when neglecting trading fees, buy-sell spreads and order delays. The overall highest predictability is obtained on the hourly trading interval for Bitcoin, yielding an accuracy of 55.74% and a cumulative return of 175.1% between October 16, 2021 and December 31, 2021. / I denna studie används ett bakåtpropagerande neoronnät (BPN) för att förutsäga prisrörelser i större kryptovalutor med hjälp av tekniska faktorer och kvantifiering av kollektivt sentimentet från mikrobloggnätverket Twitter. Vårt dataset består av dagliga, timvisa och minutvisa prisnivåer för Bitcoin, Ether och Litecoin tillsammans med 8 populära tekniska indikatorer, samt alla tweets med valutornas "cash tags" under respektive tidsperiod. Med inspiration från tidigare forskning som hävdar att artificiella nauronnät är överlägsna prognosmodeller i denna typ av analys kunde vi skapa ett system som genererar automatiska investeringsbeslut på daglig, timvis och minutvis basis. Vi hävdar med denna studie att pristrender är förutsägbara för dessa kryptovalutor, med en korrekt förutsägelsefrekvens på över 50% för alla modeller, och med lönsamma handelsstrategier för alla valutor på timbasis när man bortser från handelsavgifter, köp- och säljspreadar och orderfördröjningar. Den högsta förutsägbarheten erhålls på timhandelsintervallet för Bitcoin, vilket ger en nogrannhet på 55,74% och en ackumulerad avkastning på 175,1% mellan den 16 oktober 2021 och den 31 december 2021.

We investigate the novel idea of using computer vision to predict future stock price movement, which is performed by training a convolutional neural network (CNN) to detect patterns in images of stock graphs. Subsequently, we create a portfolio strategy based on the CNN stock price predictions to see if these predictions can generate alpha for investors. We apply this method in the Swedish stock market and evaluate the performance of CNN portfolios across two different exchanges and various stock indices segmented by market capitalisation. Our findings show that trading based on CNN predictions can outperform our benchmarks and generate positive alpha. Most of our portfolios generate positive alpha before transaction costs, while one also generates positive alpha after deducting transaction costs. Further, our results demonstrate that CNN models are capable of successfully generalising their trained knowledge, being able to detect information in stock graphs it has never seen before. This suggests that CNN models are not limited to the characteristics present in their training data, indicating that models trained under one set of market conditions can also be effective in a different market scenario. Our resultsfurther strengthen the overall findings of other researchers utilising similar methods as ours.

The South African white maize market is considered to be significantly more volatile than any other agricultural product traded on the South African Futures Exchange (SAFEX). This accentuates the need to effectively manage price risk, by means of hedging, to ensure a more profitable and sustainable maize production sector (Geyser, 2013:39; Jordaan, Grové, Jooste, A. & Jooste, Z.G., 2007:320). However, hedging at lower price levels might result in significant variation margins or costly buy–outs in order to fulfil the contract obligations. This challenge is addressed in this study by making use of technical analysis, focusing on the development of a practical and applicable composite technical indicator with the purpose of improving the timing of price risk management decisions identified by individual technical indicators. This may ultimately assist a producer in achieving a higher average hedge level compared to popular individual technical indicators. The process of constructing a composite indicator was commenced by examining the prevailing tendency of the market. By making use of the Directional Movement Index (DMI), as identified in the literature study, the market was found to continually shift between trending prices (prices moving either upwards or downwards) and prices trading sideways. Consequently, implementing only a leading (statistically more suitable for trading markets) or lagging (statistically more suitable for trending markets) technical indicator may generate false sell signals, as demonstrated by the application of these technical indicators in the white maize market. This substantiated the motivation for compiling a composite indicator that takes both leading and lagging indicators into account to more accurately identify hedging opportunities. The composite indicator made use of the Relative Strength Index (RSI) and Stochastic oscillator as leading indicators, and the Exponential Moving Average (EMA) and Moving Average Convergence Divergence (MACD) as lagging indicators. The results validated the applicability of such a composite indicator, as the composite indicator outperformed the individual technical indicators in the white maize market. The composite indicator achieved the highest average hedge level, the lowest average sell signals generated over the entire period, as well as the highest average hedge level as a percentage of the maximum price over the entire period. Hence, the composite indicator recognised hedging opportunities more accurately compared to individual technical indicators, which ultimately led to higher achieved hedging levels. / MCom. (Risk management), North-West University, Potchefstroom Campus, 2014

The South African white maize market is considered to be significantly more volatile than any other agricultural product traded on the South African Futures Exchange (SAFEX). This accentuates the need to effectively manage price risk, by means of hedging, to ensure a more profitable and sustainable maize production sector (Geyser, 2013:39; Jordaan, Grové, Jooste, A. & Jooste, Z.G., 2007:320). However, hedging at lower price levels might result in significant variation margins or costly buy–outs in order to fulfil the contract obligations. This challenge is addressed in this study by making use of technical analysis, focusing on the development of a practical and applicable composite technical indicator with the purpose of improving the timing of price risk management decisions identified by individual technical indicators. This may ultimately assist a producer in achieving a higher average hedge level compared to popular individual technical indicators. The process of constructing a composite indicator was commenced by examining the prevailing tendency of the market. By making use of the Directional Movement Index (DMI), as identified in the literature study, the market was found to continually shift between trending prices (prices moving either upwards or downwards) and prices trading sideways. Consequently, implementing only a leading (statistically more suitable for trading markets) or lagging (statistically more suitable for trending markets) technical indicator may generate false sell signals, as demonstrated by the application of these technical indicators in the white maize market. This substantiated the motivation for compiling a composite indicator that takes both leading and lagging indicators into account to more accurately identify hedging opportunities. The composite indicator made use of the Relative Strength Index (RSI) and Stochastic oscillator as leading indicators, and the Exponential Moving Average (EMA) and Moving Average Convergence Divergence (MACD) as lagging indicators. The results validated the applicability of such a composite indicator, as the composite indicator outperformed the individual technical indicators in the white maize market. The composite indicator achieved the highest average hedge level, the lowest average sell signals generated over the entire period, as well as the highest average hedge level as a percentage of the maximum price over the entire period. Hence, the composite indicator recognised hedging opportunities more accurately compared to individual technical indicators, which ultimately led to higher achieved hedging levels. / MCom. (Risk management), North-West University, Potchefstroom Campus, 2014

於這篇論文中，我們運White (2000)的Reality Check與Romano and Wolf (2005)的stepwise multiple test檢測交易策略的獲利性以更正資料窺探的偏誤。不同於先前運用資料窺探法則的研究，我們的研究以技術分析及時間序列預測兩者為依歸來建立交易策略，另外我們探討的市場集中在六個主要的亞洲股票市場。大致上，我們發現鮮少證據支持技術交易策略的獲利性；於基礎分析中且考慮交易成本時，只有少數幾個獲利性交易法則出現於兩個興新市場。另外在子樣本期間中，我們發現獲利性策略的表現並不穩定且這幾年來獲利性有逐漸變弱的趨勢。在進階分析中，我們發現沒有任何交易策略表現優越於基本的買進持有策略。 / In this paper, we exam the profitability of trading strategies by using both White’s (2000) Reality Check and Romano and Wolf (2005)s’ stepwise multiple test that correct the data snooping bias. Different from previous studies with the data snooping methodology, our analysis set the universe of forecasts (trading strategies) based on both technical analysis and time series prediction, and the markets which our investigation focuses on are six major Asian stock markets. Overall we find little supportive evidence for the profitability of trading strategies. Our basic analysis shows that there are only few profitable trading strategies detected for two emerging markets while transaction costs are taken into account. Moreover, the performances of the profitable strategies are unstable and the profitability becomes much weaker in the recent years as we find in the sub-periods. In further analysis, we also find that there is no trading strategies in our universe that can outperform the basically buy and hold strategy.

The master's thesis deals with the fundamental and technical analysis of stock Philip Morris ČR. The aim of the thesis is a specification of investment recommendation. The fundamental analysis specifies an intrinsic value of the stock Philip Morris ČR, compares it with a real stock price and recommends sale or purchase of this stock. Technical analysis uses graphical methods and technical indicators to determine an appropriate time to sale or purchase this stock.

Submitted by Verena Pereira (verenagoncalves@uefs.br) on 2018-09-12T21:52:11Z No. of bitstreams: 1 Vers?o Final_Disserta??o.pdf: 2988104 bytes, checksum: c27862945e0f83da183a6c591a54de39 (MD5) / Made available in DSpace on 2018-09-12T21:52:11Z (GMT). No. of bitstreams: 1 Vers?o Final_Disserta??o.pdf: 2988104 bytes, checksum: c27862945e0f83da183a6c591a54de39 (MD5) Previous issue date: 2018-06-15 / Trading systems based on trend following strategies are applied by many investors when negotiating in the variable income markets, in operations conducted in several asset classes worldwide. These systems play an important role in investor decision-making process, but still require further study. In this dissertation, four trend following trading systems are presented, whose performances have been demonstrated in order to evaluate their effectiveness in the Brazilian variable income market. Two of the four proposed systems were evaluated in the stock market and the other two were considered for the future contract market. For this purpose, a historical series of asset prices available for trade between January 1995 and December 2014 at the S?o Paulo Mercantile and Futures Exchange. Through simulations, the systems showed that if they were traded on the stock market and futures markets in Brazil, they would generate profitability, indicating the existence of several trends in the assets studied, obtaining a performance superior to strategy of buying and hold in the market Ibovespa index. This study contributes to the discussion on the effectiveness of trading systems based on the trend following investment philosophy / Sistemas de negocia??o baseados em estrat?gias fundamentadas no trend following, s?o utilizados por in?meros investidores para negociarem nos mercados de renda vari?vel, em opera??es nas mais variadas classes de ativos no mundo. Esses sistemas desempenham papel importante na tomada de decis?o por parte de um investidor na realiza??o de uma negocia??o, no entanto, ainda precisam de maiores estudos. Nesta disserta??o, apresentamos quatro trading systems seguidores de tend?ncias, os quais tiveram suas performances demonstradas na perspectiva de avaliar a efic?cia desses trading systems no mercado de renda vari?vel brasileiro. Dois dos quatro sistemas propostos, foram avaliados no mercado de a??es e os outros dois foram considerados para opera??es no mercado de contratos futuros. Para tanto, foram consideradas s?ries hist?ricas de pre?os de ativos dispon?veis para negocia??o entre janeiro de 1995 ? dezembro de 2014, na Bolsa de Valores Mercadorias e Futuros de S?o Paulo. Atrav?s de simula??es, os sistemas demonstraram que caso fossem operados no mercado de a??es e/ou de futuros do Brasil, gerariam lucros, indicando-se a exist?ncia de diversas tend?ncias nos ativos estudados, obtendo-se performance superior ? estrat?gia de comprar e manter no ?ndice Ibovespa. O presente trabalho contribui na discuss?o a respeito da efic?cia de sistemas de negocia??o baseados na filosofia de investimento do trend following

My diploma thesis discusses the power and importance of money management when trading foreign exchange market. With the help of real examples it aims to demonstrate the difficulty of the future foreign exchange rate estimation and the ambiguousness of the market analyses results. Comparing the results of real trading in the spirit of diversification to the actual results of Vince's model, the thesis points out whether the application of diversification is a real necessity or not. The thesis also highlights the impact of diversification on the trading system performance compared to Vince's model. The final part proposes a possible practical accounting solution to the foreign exchange speculative trades.

本研究利用雲端運算的技術，發展大量使用者使用的策略交易的系統。為滿足大量使用者的運算需求，本系統包括幾項特性： 1. 採用服務導向架構以充分使用雲端運算的特性。 2. 建立非同步事件控制機制以提供服務間非同步運算能力。 3. 採用集中式資料結構，提出收縮式肋骨網絡(SRN)資料結構，減少運算需求。 4. 提供基因演算模擬環境，讓使用者可以發展符合個人投資偏好的投資策略。 / In this study, we designed a algorithmic trading system for large numbers of users on a cloud computing plateform. So the main features of the algorithmic trading system have been as follows. 1. The use of Service-Oriented architecture in order to fully use the characteristics of cloud computing. 2. The establishment of asynchronous event control mechanism to provide services to non-synchronous computing power. 3. A centralized data structure, proposed Systolic Ribs Network (SRN) data structure, reducing the computing needs. 4. To provide the genetic algorithm simulation environment that allows users to develop in line with the investment strategy personal investment preferences.

本文改良金融投資技術分析操作方法中, 傳統的「單一移動平均」選股法為「多重移動平均」選股法, 其係以道氏理論上, 所謂的市場同時存在三種趨勢 (主要趨勢, 次級趨勢, 小型趨勢) 為基礎, 建立多重時間架構, 輔以移動平均線為股價趨勢判斷, 以及葛蘭碧八大法則之股價突破 (或跌破) 判斷原則作為操作訊號, 所彚整而提出。實證上, 採用2014年12月31日台灣證券交易所公告之台灣50、中型100, 以及富櫃50成分股為樣本, 並以2001年1月1日至2014年12月31日為回溯期間。在進行策略交易的模擬分析與績效差異檢定後, 實證結果發現, 多重移動平均選股法投資策略績效, 在統計分析上並無法較單一周期投資策略績效為優, 但卻能有效過濾沒必要的交易行為, 使突破買進之假訊號降低, 間接的降低交易次數及減少交易成本。 / This study enhanced from the traditional single period moving average for stock selection into multiple-period moving average counterpart. The theoretical foundation comes from the Dow Theory, which states that there exist three trends simultaneously, that is, major trend, secondary trend, and minor trend. Also, the Granville Rules suggest stock price breaking out may serve as entry and exit signal for trading. Our sample are grouped into three subsamples, Taiwan 50, Mid-Cap 100, Gretai 50. The sample period ranges from 2001/1/1 to 2014/12/31. Our empirical backtesting and performance test suggests that, contrary to our expectations, the multiple period method does not outperform its single period counterpart. However, the multiple period stock selection method may filter out false signals, and thereby reduce not only possible price risk associated with noisy trades but the accompanying transaction costs. / 摘要 I Abstract II 致謝詞 III 目錄 V 圖次 VII 表次 VIII 第一章　緒論 1 第一節　研究背景及動機 1 第二節　研究目的 2 第三節　研究對象與範圍 2 第四節　研究流程 4 第二章　文獻回顧 6 第一節　技術分析理論 6 一、技術分析基本邏輯 6 二、技術分析主要的型態類型 7 第二節　移動平均線的原理 9 一、簡單移動平均線的計算 9 二、移動平均線的常見應用 9 第三節　多重移動平均理論及選股法 11 一、多重移動平均的原理 11 二、多重移動平均的選股模式 11 第四節　相關研究文獻回顧與評析 11 一、過去研究文獻 11 二、文獻評析 16 三、本文假說推論 16 第三章　研究方法 17 第一節　傳統移動平均線選股模式 17 第二節　YC指標選股模式 17 第三節　選股模式績效差異檢定 19 第四節　資料來源與變數選取 19 第四章　實證分析 20 第一節　操作策略績效估計 20 第二節　操作策略績效比較 28 第三節　多重策略模型之適性歸納–由規模的角度 36 第五章　結論與建議 43 參考文獻 44 中文部份 44 英文部份 46 參考網址 46 圖次 圖1-4-1 研究流程圖 5 圖2-1-1 型態類技術理論的基本分類 6 圖2-1-2 市場同時存在三種趨勢 7 圖2-1-3 K線的基本構造 8 圖2-2-1 葛蘭碧(Granville)八大法則概念圖 10 表次 表1-3-1 台股之台灣50成分股 2 表1-3-2 台股之中型100成分股 3 表1-3-3 台股之富櫃50成分股 3 表2-4-1 過去研究文獻的整理 14 表4-1-1 台灣50成份股總交易次數及成本 20 表4-1-2 中型100成份股總交易次數及成本 22 表4-1-3 富櫃50成份股總交易次數及成本 26 表4-1-4 單一與多重模式下交易次數與進出場交易成本彚整 28 表4-2-1 台灣50成份股總報酬及總報酬率 28 表4-2-2 中型100成份股總報酬及總報酬率 30 表4-2-3 富櫃50成份股總報酬及總報酬率 34 表4-2-4 單一與多重策略下的平均總報酬與平均總報酬率彚整 36 表4-3-1 多重策略下總報酬率與市值之迴歸分析 36 表4-3-2 多重策略下總報酬率與股本之迴歸分析 37 表4-3-3 台灣50股本前20%成份股之策略績效及差異比較 37 表4-3-4 台灣50股本後20%成份股之策略績效及差異比較 38 表4-3-5 中型100股本前20%成份股之策略績效及差異比較 39 表4-3-6 中型100股本後20%成份股之策略績效及差異比較 40 表4-3-7 富櫃50股本前20%成份股之策略績效及差異比較 41 表4-3-8 富櫃50股本後20%成份股之策略績效及差異比較 42