Implementasi Penggunaan Sistem Pakar pada Trading Forex Jenis Locco

Pada era global seperti sekarang ini, perdagangan tidak lagi dilakukan dengan cara tradisional tetapi juga sudah mulai menggunakan transaksi secara online untuk memperoleh barang atau jasa yang dibutuhkan. Perdagangan yang saat ini sering banyak dilakukan adalah tipe future trading dan harga selalu bergerak mengikuti volatilitas pasar. Locco atau perdagangan emas future menjadi objek penelitian ini sebagai salah satu bagian dalam perdagangan future. Tentunya dalam menganalisis pergerakan harga para trader akan menggunakan kombinasi dari 3 analisis, yaitu fundamental, tenikal, dan sentimen pasar. Bagi para trader yang kurang menguasai tentu akan kesulitan dalam memprediksi perkiran harga yang tepat. Banyaknya data yang harus dibaca dan keterbatasan kemampuan para trader dalam mengolah informasi menjadikan permasalahan tersendiri dalam pengambilan keputusan. Oleh karena itu, robot forex sebagai salah satu produk pengembangan dari sistem pakar ini diperlukan. Tujuan dari pembuatan robot forex ini adalah untuk membantu para trader dalam pengambilan keputusan untuk waktu yang tepat dalam bertransaksi trading dengan mengambil posisi buy atau sell sehingga kerugian bisa diminimalkan. Selain itu, poses pemrosesan data juga jadi lebih cepat sehingga hasil diharapkan bisa lebih akurat dalam memprediksi pergerakan harga yang akan datang dan dapat memberikan sinyal yang tepat kepada trader. Metode yang akan digunakan dalam pembuatan robot forex ini menggunakan metode pengembangan waterfall dan pengolahan data dengan menggunakan indikator MA sebagai dasar acuan dalam memprediksi perkiraan harga. Pada akhirnya, hasil proses pengolahan data tersebut, dalam hal pengambilan keputusan bisa dilakukan by system atau secara manual oleh user itu sendiri

Pengaruh Kinerja Keuangan dan Indikator Kesulitan Finansil terhadap Harga Saham Bank : Studi Kasus Bank Bca

The purpose this research are to provide data, information and deep analysis about finance of go public Bank in Indonesia, include their financial distress and stock price. Quantitative approach is used to analyze the impact of bank performance, financial distress and bankruptcy prediction to bank\u27s stock price. We use ordinary least square regression (OLS) method with all of the OLS method requirement such as normality test, multicollinearity test, autocorrelation test and heteroschedasticity test. We use secondary data from bank financial report and bank stock price from January 2004 until October 2010. We use 32 variables that represent five main variables in the model. The result showed that liquidity have negative impact to stock price, income have positive impact to stock price and more debt will increase bank credibility and attract more investor to buy the stock and will increase bank stock price

APLICACIÓN DE LAS REDES NEURONALES EN EL ANÁLISIS DEL PRECIO DEL MAÍZ

El propósito de este trabajo fue analizar el impacto del precio nacional de otros productos relacionados y del precio internacional del maíz sobre el comportamiento del precio interno del maíz a través de dos redes neuronales, las cuales demostraron tener un alto poder predictivo del precio nacional del maíz en 5 y 3 años hacia adelante. Se realizó un análisis de sensibilidad utilizando el algoritmo de Garson y se pudo demostrar que el arroz, el cártamo y el sorgo son productos complementarios al maíz, siendo el precio del arroz la variable con mayor impacto positivo sobre el precio del maíz; así como también que el trigo, la soya y la cebada se comportan como productos sustitutos del maíz, siendo el precio del trigo el que mayor impacto tiene sobre el precio del maíz. Incluyendo el precio internacional del maíz el comportamiento de las demás variables se mantuvo, y se obtuvo una sensibilidad positiva entre el precio nacional e internacional del maíz. Finalmente, el estudio mostró la aplicación de los modelos de RNA sobre el precio de un producto en particular, con la posibilidad de utilizarse en el proceso de toma de decisiones en las políticas públicas encaminadas al apoyo de los productores agrícolas

Sentiment Analysis for Long-Term Stock Prediction

abstract: There have been extensive research in how news and twitter feeds can affect the outcome of a given stock. However, a majority of this research has studied the short term effects of sentiment with a given stock price. Within this research, I studied the long-term effects of a given stock price using fundamental analysis techniques. Within this research, I collected both sentiment data and fundamental data for Apple Inc., Microsoft Corp., and Peabody Energy Corp. Using a neural network algorithm, I found that sentiment does have an effect on the annual growth of these companies but the fundamentals are more relevant when determining overall growth. The stocks which show more consistent growth hold more importance on the previous year’s stock price but companies which have less consistency in their growth showed more reliance on the revenue growth and sentiment on the overall company and CEO. I discuss how I collected my research data and used a multi-layered perceptron to predict a threshold growth of a given stock. The threshold used for this particular research was 10%. I then showed the prediction of this threshold using my perceptron and afterwards, perform an f anova test on my choice of features. The results showed the fundamentals being the better predictor of stock information but fundamentals came in a close second in several cases, proving sentiment does hold an effect over long term growth.Dissertation/ThesisMasters Thesis Computer Science 201

Feature learning for stock price prediction shows a significant role of analyst rating

Data Availability Statement: The code is available from https://mkhushi.github.io/ (accessed on 1 February 2021). Dataset License: License under which the dataset is made available (CC0).Efficient Market Hypothesis states that stock prices are a reflection of all the information present in the world and generating excess returns is not possible by merely analysing trade data which is already available to all public. Yet to further the research rejecting this idea, a rigorous literature review was conducted and a set of five technical indicators and 23 fundamental indicators was identified to establish the possibility of generating excess returns on the stock market. Leveraging these data points and various classification machine learning models, trading data of the 505 equities on the US S&P500 over the past 20 years was analysed to develop a classifier effective for our cause. From any given day, we were able to predict the direction of change in price by 1% up to 10 days in the future. The predictions had an overall accuracy of 83.62% with a precision of 85% for buy signals and a recall of 100% for sell signals. Moreover, we grouped equities by their sector and repeated the experiment to see if grouping similar assets together positively effected the results but concluded that it showed no significant improvements in the performance—rejecting the idea of sector-based analysis. Also, using feature ranking we could identify an even smaller set of 6 indicators while maintaining similar accuracies as that from the original 28 features and also uncovered the importance of buy, hold and sell analyst ratings as they came out to be the top contributors in the model. Finally, to evaluate the effectiveness of the classifier in real-life situations, it was backtested on FAANG (Facebook, Amazon, Apple, Netflix & Google) equities using a modest trading strategy where it generated high returns of above 60% over the term of the testing dataset. In conclusion, our proposed methodology with the combination of purposefully picked features shows an improvement over the previous studies, and our model predicts the direction of 1% price changes on the 10th day with high confidence and with enough buffer to even build a robotic trading system.This research received no external funding

Prediction of Multiple Time Series at Stock Market Trading

V diplomové práci je uveden všeobecný postup používaný pro předpověď časových řad, jejich rozdělení, základní charakteristiky a základní statistické metody pro jejich předpovídaní. Spomenuty jsou také neuronové sítě a jejich dělení s ohledem na vhodnost k předpovídaní časových řad. Je navrhnut a implementován program pro predikci vývoje více časových řad při burzovním obchodování, kterého základem je model flexibilního neuronového stromu, kterého struktura je optimalizována pomocí imunitního programování a parametry pomocí modifikované verze simulovaného žíhání anebo pomocí optimalizace hejnem částic. Program je nejdříve testován na schopnosti předpovídat jednoduché časové řady a nakonec je testována jeho schopnost předpovídat více časových řad.The diploma thesis comprises of a general approach used to predict the time series, their categorization, basic characteristics and basic statistical methods for their prediction. Neural networks are also mentioned and their categorization with regards to the suitability for prediction of time series. A program for the prediction of the progress of multiple time series in stock market is designed and implemented, and it's based on a model of flexible neuron tree, whose structure is optimized using immune programming and parameters using a modified version of simulated annealing or particle swarm optimization. Firstly, the program is tested on its ability to predict simple time series and then on its ability to predict multiple time series.