38 research outputs found
2017 GREAT Day Program
SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp
Scaling energy management in buildings with artificial intelligence
L'abstract è presente nell'allegato / the abstract is in the attachmen
From Prediction to Profit: Evaluating S&P 500 Forecasting Models Using Machine Learning
Αυτή η πτυχιακή εργασία παρουσιάζει μια ολοκληρωμένη διερεύνηση στην πρόβλεψη των
τιμών κλεισίματος του S&P 500 χρησιμοποιώντας μια πληθώρα μοντέλων πρόβλεψης και
στρατηγικών συναλλαγών. Αξιοποιώντας τις εξελίξεις στη Χρηματοοικονομική Τεχνολογία (FinTech), χρησιμοποιεί μεθοδολογίες Στατιστικές Μοντελοποίησης (ARIMA), Μηχανικής Μάθησης (Support Vector Regression, SVR) και Βαθιάς Μάθησης (Long Short-Term
Memory, LSTM) για να προβλέψει τις τιμές κλεισίματος του S&P 500. Αυτά τα μοντέλα
εφαρμόζονται σε τέσσερις τύπους δεδομένων, καθένας από τους οποίους διαφοροποιείται ανάλογα με τους ορίζοντες πρόβλεψης και τα σύνολα χαρακτηριστικών.
Η έρευνα ξεκινά με την απόκτηση και προεπεξεργασία χρηματοοικονομικών και οικονομικών δεδομένων, ακολουθούμενη από επιλογή χαρακτηριστικών, μετά την οποία κατασκευάζονται και εκπαιδεύονται διάφορα μοντέλα. Η προγνωστική απόδοση αυτών των μοντέλων στη συνέχεια αξιολογείται παραδοσιακά, και δοκιμάζεται επίσης σε διαφορετικούς
αλγορίθμους επενδυτικών στρατηγικών για προσομοίωση συναλλαγών. Αυτό προσφέρει
διπλή αξιολόγηση των δυνατοτήτων των μοντέλων, τόσο από την άποψη της προγνωστικής ακρίβειας όσο και από την άποψη της απόδοσης συναλλαγών.
Η έρευνα προσφέρει πληροφορίες για την εφαρμογή της τεχνολογίας στις χρηματοοικονομικές προβλέψεις και συναλλαγές, και στοχεύει να συμβάλει στο υπάρχον σύνολο γνώσεων στους τομείς της προγνωστικής μοντελοποίησης. Επιπλέον, χρησιμεύει ως ένα εκπαιδευτικό ταξίδι για όποιον θέλει να εξερευνήσει τον κόσμο των προβλέψεων χρηματιστηρίου και της διαμόρφωσης στρατηγικής συναλλαγών.
Τα αποτελέσματα αυτής της συγκριτικής ανάλυσης δείχνουν ότι κάθε ένα από τα μοντέλα
έχει τα μοναδικά πλεονεκτήματά του σε συγκεκριμένα σενάρια αγοράς. Ωστόσο, συνολικά,
το μοντέλο LSTM ξεπέρασε σταθερά τα άλλα στην πρόβλεψη των τιμών κλεισίματος του
S&P 500, καθώς και στην απόδειξη της αποτελεσματικότητάς του στις προσομοιωμένες
στρατηγικές συναλλαγών, επιβεβαιώνοντας τις δυνατότητες των προσεγγίσεων βαθιάς
μάθησης στις χρηματοοικονομικές προβλέψεις. Το SVR, ενώ παρουσίαζε καλά λάθη πρόβλεψης, η απόδοσή του στις προσομοιώσεις συναλλαγών ήταν σχετικά κατώτερη. Εν τω
μεταξύ, το παραδοσιακό μοντέλο ARIMA, αν και κατατάσσεται τελευταίο στις περισσότερες
συγκρίσεις, έκανε μια αξιέπαινη προσπάθεια για μια στατιστική προσέγγιση, τονίζοντας τη
συνεχιζόμενη συνάφειά του στις χρηματοοικονομικές προβλέψεις.
Τα ευρήματα αυτής της έρευνας αποσκοπούν να εμπνεύσουν περαιτέρω εξερεύνηση και
βελτιώσεις στο συνδυασμό των οικονομικών και της τεχνολογίας.This thesis presents a comprehensive exploration into the prediction of the S&P 500 closing prices using a multitude of prediction models and trading strategies. Capitalizing on
the advancements in Financial Technology (FinTech), it employs Statistical (ARIMA), Machine Learning (Support Vector Regression, SVR), and Deep Learning (Long Short-Term
Memory, LSTM) methodologies to forecast the S&P 500 closing prices. These models
are applied to four types of data, each differentiated by forecasting horizons and sets of
features.
The study starts with the acquisition and preprocessing of financial and economic data,
followed by feature selection, after which various models are built and trained. The predictive performance of these models is then evaluated traditionally and also tested in different
trading simulation algorithms. This offers a dual lens to assess the models’ capabilities,
both from a predictive accuracy perspective and a trading performance viewpoint.
The research offers insights into the application of technology in financial prediction and
trading, and aims to contribute to the existing body of knowledge in the areas of predictive
modeling. Moreover, it serves as an educative journey for anyone seeking to explore the
world of stock market predictions and trading strategy formulation.
The results of this comparative analysis show that each of the models has its unique
strengths in specific market scenarios. However, overall, the LSTM model consistently
outperformed others in predicting the S&P 500’s closing prices, as well as proving its
efficiency in the simulated trading strategies, affirming the potential of deep learning approaches in financial forecasting. The SVR, while demonstrating good forecasting errors,
its performance in trading simulations was relatively inferior. Meanwhile, the traditional
ARIMA model, although ranking last in most of the comparisons, made a commendable
effort for a statistical approach, highlighting its continued relevance in financial prediction.
The findings from this research are hoped to inspire further exploration and improvements
in the combination of finance and technology
Understanding Quantum Technologies 2022
Understanding Quantum Technologies 2022 is a creative-commons ebook that
provides a unique 360 degrees overview of quantum technologies from science and
technology to geopolitical and societal issues. It covers quantum physics
history, quantum physics 101, gate-based quantum computing, quantum computing
engineering (including quantum error corrections and quantum computing
energetics), quantum computing hardware (all qubit types, including quantum
annealing and quantum simulation paradigms, history, science, research,
implementation and vendors), quantum enabling technologies (cryogenics, control
electronics, photonics, components fabs, raw materials), quantum computing
algorithms, software development tools and use cases, unconventional computing
(potential alternatives to quantum and classical computing), quantum
telecommunications and cryptography, quantum sensing, quantum technologies
around the world, quantum technologies societal impact and even quantum fake
sciences. The main audience are computer science engineers, developers and IT
specialists as well as quantum scientists and students who want to acquire a
global view of how quantum technologies work, and particularly quantum
computing. This version is an extensive update to the 2021 edition published in
October 2021.Comment: 1132 pages, 920 figures, Letter forma
Play Among Books
How does coding change the way we think about architecture? Miro Roman and his AI Alice_ch3n81 develop a playful scenario in which they propose coding as the new literacy of information. They convey knowledge in the form of a project model that links the fields of architecture and information through two interwoven narrative strands in an “infinite flow” of real books
Efficiency and Anomalies in Stock Markets
The Efficient Market Hypothesis believes that it is impossible for an investor to outperform the market because all available information is already built into stock prices. However, some anomalies could persist in stock markets while some other anomalies could appear, disappear and re-appear again without any warning. A Special Issue on "Efficiency and Anomalies in Stock Markets" will be devoted to advancements in the theoretical development of market efficiency and anomaly in the Stock Market, as well as applications in Stock Market efficiency and anomalies
Data Science: Measuring Uncertainties
With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems
Machine Learning Methods to Exploit the Predictive Power of Open, High, Low, Close (OHLC) Data
Novel machine learning techniques are developed for the prediction of financial markets, with a combination of supervised, unsupervised and Bayesian optimisation machine learning methods shown able to give a predictive power rarely previously observed. A new data mining technique named Deep Candlestick Mining (DCM) is proposed that is able to discover highly predictive dataset specific candlestick patterns (arrangements of open, high, low, close (OHLC) aggregated price data structures) which significantly outperform traditional candlestick patterns. The power that OHLC features can provide is further investigated, using LSTM RNNs and XGBoost trees, in the prediction of a mid-price directional change, defined here as the mid-point between either the open and close or high and low of an OHLC bar. This target variable has been overlooked in the literature, which is surprising given the relative ease of predicting it, significantly in excess of noisier financial quantities. However, the true value of this quantity is only known upon the period's ending – i.e. it is an after-the-fact observation. To make use of and enhance the remarkable predictability of the mid-price directional change, multi-period predictions are investigated by training
many LSTM RNNs (XGBoost trees being used to identify powerful OHLC input feature combinations), over different time horizons, to construct a Bayesian optimised trend prediction ensemble. This fusion of long-, medium- and short-term information results in a model capable of predicting market trend direction to greater than 70% better than random. A trading strategy is constructed to demonstrate how this predictive power can be used by exploiting an artefact of the LSTM RNN training process which allows the trading system to size and place trades in accordance with the ensemble's predictive certainty
Advances in Public Transport Platform for the Development of Sustainability Cities
Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency