Penerapan Metode Jaringan Syaraf Tiruan Backpropagation Untuk Meramalkan Harga Saham (IHSG)

Pada saat sekarang ini banyak sekali perusahaan atau lembaga – lembaga yang telah menganggap penting peran pasar modal dalam bidang perekonomian. Jenis pembelian modal yang masih banyak dilakukan adalah seperti pembelian tanah, emas, atau uang, sedangkan pembelian modal saham adalah pembelian yang sah dan sangat menarik untuk dilakukan. Modal harga saham sangat menarik dilakukan karena kita dapat memilih model investasinya, yaitu bisa jangka panjang atau jangka pendek. Para investor akan sangat terbantu dengan adanya sebuah model peramalan untuk meramalakan harga saham. System peramalan ini sangat berguna karena dengan adanya system peramalan tersebut investor dapat mengurangi nilai kerugian dan dapat memaksimalkan keuntungan. System peramalan harga saham ini akan dibuat menggunakan metode Jaringan Syaraf Tiruan Backpropagation. Jaringan Syaraf Tiruan Backpropagation adalah algoritma belajar, jadi metode ini akan melatih jaringan – jaringan pada situasi sebelumnya. Jaringan tersebut berupa data history yang dapat mempengaruhi harga saham, seperti harga saham itu sendiri ditambah dengan harga emas dan minyak dunia. Pelatihan akan menyesuaikan bobot dalam jaringan sebagai input baru untuk meramalkan harga saham. Penelitian ini mendapatkan hasil akurasi sebesar 99,98% dan mean square error(MSE) sebesar 0,9915. Artinya, peramalan dengan keakuratan yang tinggi dapat dicapai menggunakan metode Jaringan Syaraf Tiruan Backpropagation

Borsa Endeksi Hareketlerinin Tahmini: Trend Belirleyici Veri

Bu çalışma BIST 100 borsa endeksinin negatif ve pozitif yönlü hareketlerinin tahmin edilmesini konu edinmektedir. Yapay sinir ağı, destek vektör makinesi ve naive Bayes algoritmasının tahmin performansları karşılaştırılmaktadır. Analizler iki aşamalı olarak yapılmaktadır. Birinci aşamada tahmin modellerinde girdi olarak kullanılacak dokuz adet teknik gösterge, borsa endeksi açılış, kapanış, en yüksek ve en düşük fiyatlar, kullanılarak hesaplanmakta ve sürekli olan bu teknik göstergeler barındırdıkları trende göre kategorize edilerek yeni bir veri seti oluşturulmaktadır. İkinci aşamada ise, trend belirleyici veri seti girdi olarak kullanılmakta ve seçilen üç makine öğrenme algoritması kullanılarak tahminler yapılmaktadır. BIST 100 veri seti 2009-2018 Aralığını kapsayan günlük kapanış fiyatlarını içermektedir. Analizlerle, destek vektör makineleri algoritmasının en iyi sınıflandırıcı olduğu sonucuna ulaşılmıştır. Ayrıca, daha önceki benzer çalışmalarla karşılaştırmalar yapılarak gerek kullanılan veri seti gerekse tahmin modellerinin etkileri tartışılmaktadır

Revising empirical linkages between direction of Canadian stock price index movement and Oil supply and demand shocks: Artificial neural network and support vector machines approaches

Over the years, the oil price has shown an impressive fluctuation and isn’t without signification impact on the evolution of stock market returns. Because of the complexity of stock market data, developing an efficient model for predicting linkages between macroeconomic data and stock price movement is very difficult. This study attempted to develop two robust and efficient models and compared their performance in predicting the direction of movement in the Canadian stock market. The proposed models are based on two classification techniques, artificial neural networks and Support Vector Machines. Considering together world oil production and world oil prices in order to supervise for oil supply and oil demand shocks, strong evidence of sensitivity of stock price movement direction to the oil price shocks specifications is found. Experimental results showed that average performance of artificial neural networks model is around 96.75% that is significantly better than that of the Support Vector Machines reaching 95.67%

Revising empirical linkages between direction of Canadian stock price index movement and Oil supply and demand shocks: Artificial neural network and support vector machines approaches

Over the years, the oil price has shown an impressive fluctuation and isn’t without signification impact on the evolution of stock market returns. Because of the complexity of stock market data, developing an efficient model for predicting linkages between macroeconomic data and stock price movement is very difficult. This study attempted to develop two robust and efficient models and compared their performance in predicting the direction of movement in the Canadian stock market. The proposed models are based on two classification techniques, artificial neural networks and Support Vector Machines. Considering together world oil production and world oil prices in order to supervise for oil supply and oil demand shocks, strong evidence of sensitivity of stock price movement direction to the oil price shocks specifications is found. Experimental results showed that average performance of artificial neural networks model is around 96.75% that is significantly better than that of the Support Vector Machines reaching 95.67%

Imprinting effects, managerial knowledge and the internationalisation of small and medium size enterprises from emerging economies

This thesis examines the internationalisation behaviour of small and medium size enterprises (SMEs) from emerging economies. In summary, the thesis comprises of five chapters: First, Chapter 1 provides a brief introduction to the full thesis. Chapter 2 systematically reviews 55 selected articles, first examining the underlying reasons why SMEs in emerging markets internationalise, followed by their corresponding barriers. Concurrently, by examining theories that have been used to study the internationalisation of SMEs from emerging markets, findings from the literature are analysed. Findings suggest that through collaborations, in the form of networks, these SMEs have been able to indulge their resources, and in turn benefit from superior impacts on their overall performance. The management of information, knowledge and collaboration is therefore re-emphasised in this review, to ensure the success of emerging markets SMEs’ internationalisation. The analysis on this review provides valuable input on research suggestions and directions for future work in this area. Next, Chapter 3 discusses the issue of whether a firm’s ‘home’ environment influences SMEs’ scope of internationalisation. This chapter uses institutional and organisational imprinting theories to argue that emerging market SMEs born during the market liberalisation period are likely to have a greater scope of internationalisation than those founded in other periods. It also argues that this effect is moderated by the SMEs’ size, its dispersed ownership structure, and its geographical diversification. Hypotheses are tested using a sample of 177 Indian SMEs collected using secondary data from the Bureau Van Dijk’s ORBIS database. Results support the hypothesis on the relationship between home-market liberalisation imprinting and SMEs’ scope of internationalisation. Findings also support that the moderating effect of SMEs’ size, geographical diversification and ownership dispersion reduces the imprinting effect of the above relationship. Chapter 4 is about the relationship between SMEs’ managerial knowledge (i.e., foreign institutional knowledge, foreign business knowledge, foreign supply chain knowledge, and internationalisation knowledge) and their financial and non-financial performance. It examines the above link based on data collected from questionnaire survey responses of 295 SMEs from India involved in internationalisation. Research findings suggest that (1) SMEs’ managerial knowledge has a direct impact on their financial and non-financial performance, and that (2) SMEs financial performance plays a mediating role between their managerial knowledge and their non-financial performance. Hypotheses are based on the knowledge-based view of internationalisation, and the chapter provides deeper insights into the role of managerial knowledge on emerging-market SMEs’ internationalisation performance. Finally, Chapter 5 includes a discussion and conclusions of research findings from the PhD study. First, it describes how the research questions mentioned in the introduction chapter were addressed. Second, some suggestions and recommendations are given for continuation of the work presented in this thesis

Sistema inteligente para la predicción del precio diario de las acciones mineras en la Bolsa de Valores de New York usando un modelo híbrido de redes neuronales y máquina de soporte vectorial de regresión

Predecir el precio de una acción es un tema muy importante en el mundo financiero, debido a que mediante ella se puede generar una estrategia de inversión y obtener muchas ganancias. El comportamiento de los precios de las acciones sigue una distribución muy compleja, siendo afectadas por factores internos de las compañías, tales como decisiones gerenciales, y también por factores externos, como el estado del mercado en un momento dado. El sector minero es considerado uno de los sectores más volátiles dentro de la bolsa, y frecuentemente atrae a los inversionistas más arriesgados que desean obtener rápidas ganancias; sin embargo no se han encontrado estudios que se hayan enfocado en este sector. La precisión de los modelos de machine learning dependen de la correcta elección de las variables y técnicas a utilizar, así como también del pre procesamiento que se realice a la data antes de ser ingresada al modelo, es por esto que en el presente trabajo se realizó una encuesta a expertos de inversión en la bolsa de valores sobre las variables influyentes en el comportamiento de una acción minera, producto de ello se identificaron variables como el precio de los metales, precio de los índices y precio del dólar; las cuales, junto a las variables fundamentales y técnicas, participaron en la selección de variables mediante el cálculo del coeficiente de correlación de Pearson en cada una de ellas. Las variables resultantes fueron ingresadas posteriormente al modelo híbrido propuesto, donde las salidas de cada una de las técnicas de machine learning utilizadas (redes neuronales artificiales, máquinas de soporte vectorial para regresión y red neuronal de base radial) formaban parte de la entrada hacia una red neuronal artificial, considerada como técnica principal debido a que alcanzaba los mejores resultados en la fase experimental. Para validar el sistema se consideró el dataset de las empresas Buenaventura, Southern Copper, Fortuna Silver Mines, Barrick Gold Corporation y BHP Billiton Limited; que alcanzaron un MAPE de 1.666, 1.470, 1.375, 2.567 y 0.998 respectivamente, y un promedio de error de 1.615%, lo que demuestra una gran mejora con respecto al 5.4% de error obtenido en el sector más cercano (petrolero).Tesi