| Working Paper |
File Downloads |
Abstract Views |
| Last month |
3 months |
12 months |
Total |
Last month |
3 months |
12 months |
Total |
| A comparison of LSTM and GRU architectures with novel walk-forward approach to algorithmic investment strategy |
0 |
0 |
10 |
36 |
0 |
1 |
35 |
70 |
| Analysis of HF data on the WSE in the context of EMH |
1 |
1 |
2 |
48 |
1 |
1 |
5 |
217 |
| Analysis of HF data on the WSE in the context of EMH |
0 |
0 |
1 |
42 |
0 |
1 |
3 |
177 |
| Application of machine learning in quantitative investment strategies on global stock markets |
0 |
1 |
9 |
34 |
0 |
2 |
26 |
64 |
| Applying Exogenous Variables and Regime Switching To Multifactor Models on Equity Indices |
0 |
0 |
0 |
17 |
0 |
0 |
3 |
74 |
| Applying Hurst Exponent in Pair Trading Strategies |
1 |
1 |
5 |
125 |
1 |
8 |
29 |
412 |
| Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index |
0 |
0 |
3 |
10 |
3 |
10 |
27 |
47 |
| Artificial Neural Networks Performance in WIG20 Index Options Pricing |
0 |
0 |
1 |
45 |
0 |
1 |
3 |
202 |
| Can We Invest Based on Equity Risk Premia and Risk Factors from Multi-Factor Models? |
0 |
0 |
1 |
82 |
1 |
1 |
2 |
126 |
| Construction and Hedging of Equity Index Options Portfolios |
1 |
1 |
10 |
10 |
1 |
1 |
12 |
12 |
| Construction and Hedging of Equity Index Options Portfolios |
0 |
0 |
9 |
9 |
0 |
3 |
21 |
21 |
| Cross-Sectional Returns With Volatility Regimes From Diverse Portfolio of Emerging and Developed Equity Indices |
0 |
0 |
0 |
27 |
0 |
1 |
2 |
60 |
| Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index |
3 |
3 |
8 |
45 |
4 |
4 |
16 |
78 |
| Do Multi-Factor Models Produce Robust Results? Econometric And Diagnostic Issues In Equity Risk Premia Study |
0 |
0 |
1 |
16 |
0 |
0 |
1 |
137 |
| Does historical volatility term structure contain valuable in-formation for predicting volatility index futures? |
0 |
0 |
1 |
68 |
0 |
0 |
4 |
262 |
| Does the inclusion of exposure to volatility into diversified portfolio improve the investment results? Portfolio construction from the perspective of a Polish investor |
0 |
0 |
0 |
3 |
0 |
0 |
1 |
28 |
| Emerging versus developed volatility indices. The comparison of VIW20 and VIX indices |
1 |
1 |
1 |
79 |
1 |
1 |
2 |
598 |
| Enhanced Index Replication Based on Smart Beta and Tail-Risk Asset Allocation |
0 |
0 |
1 |
13 |
0 |
0 |
3 |
11 |
| Enhancing literature review with LLM and NLP methods. Algorithmic trading case |
0 |
1 |
2 |
2 |
0 |
4 |
13 |
13 |
| Enhancing literature review with NLP methods Algorithmic investment strategies case |
0 |
0 |
10 |
10 |
2 |
6 |
31 |
31 |
| Ensembled LSTM with Walk Forward Optimization in Algorithmic Trading |
1 |
1 |
6 |
48 |
3 |
4 |
20 |
80 |
| Generalized Mean Absolute Directional Loss as a Solution to Overfitting and High Transaction Costs in Machine Learning Models Used in High-Frequency Algorithmic Investment Strategies |
1 |
1 |
4 |
4 |
3 |
5 |
15 |
15 |
| Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices |
0 |
0 |
0 |
8 |
0 |
1 |
4 |
14 |
| Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices |
0 |
0 |
0 |
12 |
0 |
0 |
5 |
17 |
| High-Frequency and Model-Free Volatility Estimators |
0 |
0 |
0 |
207 |
0 |
2 |
3 |
517 |
| Hybrid Investment Strategy Based on Momentum and Macroeconomic Approach |
0 |
1 |
2 |
53 |
0 |
3 |
9 |
110 |
| Improving Realized LGD Approximation: A Novel Framework with XGBoost for Handling Missing Cash-Flow Data |
0 |
0 |
4 |
4 |
0 |
0 |
13 |
13 |
| Improving Realized LGD approximation: A Novel Framework with XGBoost for handling missing cash-flow data |
0 |
0 |
5 |
5 |
0 |
1 |
11 |
11 |
| Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data |
1 |
1 |
1 |
1 |
2 |
2 |
2 |
2 |
| Investing in VIX futures based on rolling GARCH models forecasts |
3 |
4 |
13 |
245 |
6 |
8 |
43 |
634 |
| Investment strategies beating the market. What can we squeeze from the market? |
0 |
0 |
0 |
145 |
0 |
0 |
1 |
384 |
| LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies |
0 |
2 |
13 |
13 |
4 |
7 |
32 |
32 |
| LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies |
0 |
0 |
6 |
6 |
1 |
2 |
13 |
13 |
| Machine learning in algorithmic trading strategy optimization - implementation and efficiency |
0 |
1 |
6 |
496 |
1 |
4 |
13 |
1,710 |
| Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies |
0 |
1 |
5 |
9 |
0 |
1 |
16 |
24 |
| Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies |
0 |
1 |
6 |
22 |
0 |
1 |
24 |
78 |
| Midquotes or Transactional Data? The Comparison of Black Model on HF Data |
0 |
0 |
0 |
52 |
0 |
0 |
1 |
160 |
| Momentum and contrarian effects on the cryptocurrency market |
0 |
0 |
4 |
163 |
0 |
0 |
18 |
460 |
| Optimal Markowitz Portfolio Using Returns Forecasted with Time Series and Machine Learning Models |
1 |
6 |
18 |
54 |
3 |
14 |
45 |
105 |
| Option Pricing Models with HF Data – a Comparative Study. The Properties of Black Model with Different Volatility Measures |
0 |
0 |
0 |
100 |
0 |
1 |
4 |
387 |
| Options delta hedging with no options at all |
0 |
1 |
4 |
58 |
0 |
2 |
12 |
174 |
| Predicting DJIA, NASDAQ and NYSE index prices using ARIMA and VAR models |
0 |
1 |
21 |
98 |
5 |
11 |
59 |
202 |
| Predicting prices of S&P500 index using classical methods and recurrent neural networks |
0 |
2 |
8 |
114 |
0 |
2 |
17 |
159 |
| Predictive modeling of foreign exchange trading signals using machine learning techniques |
1 |
10 |
35 |
35 |
3 |
16 |
67 |
67 |
| Quantile regression analysis to predict GDP distribution using data from the US and UK |
0 |
2 |
6 |
28 |
0 |
4 |
19 |
49 |
| REnsembling ARIMAX Model in Algorithmic Investment Strategies on Commodities Market |
3 |
6 |
17 |
26 |
4 |
15 |
42 |
54 |
| Robust optimisation in algorithmic investment strategies |
4 |
10 |
24 |
40 |
5 |
15 |
39 |
67 |
| Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market |
0 |
0 |
0 |
76 |
0 |
2 |
9 |
233 |
| Simple heuristics for pricing VIX options |
0 |
0 |
1 |
29 |
1 |
1 |
7 |
165 |
| Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market |
0 |
1 |
8 |
8 |
1 |
3 |
18 |
18 |
| Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market |
0 |
1 |
11 |
11 |
0 |
2 |
33 |
33 |
| Supervised Autoencoder MLP for Financial Time Series Forecasting |
1 |
2 |
8 |
23 |
2 |
7 |
32 |
62 |
| Supervised Autoencoder MLP for Financial Time Series Forecasting |
0 |
0 |
3 |
4 |
0 |
0 |
3 |
5 |
| Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling Enhance Predictions on Noisy Data |
0 |
0 |
3 |
3 |
0 |
3 |
7 |
7 |
| Systemic risk indicator based on implied and realized volatility |
0 |
0 |
0 |
15 |
1 |
1 |
5 |
24 |
| The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models |
0 |
1 |
20 |
20 |
2 |
9 |
53 |
53 |
| The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models |
0 |
0 |
19 |
19 |
0 |
2 |
27 |
27 |
| The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Mean Absolute Directional Loss (GMADL) and Quantile loss, are proposed and evaluated against the Buy and Hold benchmark and two benchmark strategies based on technical indicators. The evaluation is conducted using data of various frequencies: 5 minute, 15 minute, and 30 minute intervals, over the 6 different periods. Although the Informer-based model with Quantile loss did not outperform the benchmark, two other models achieved better results. The performance of the model using RMSE loss worsens when used with higher frequency data while the model that uses novel GMADL loss function is benefiting from higher frequency data and when trained on 5 minute interval it beat all the other strategies on most of the testing periods. The primary contribution of this study is the application and assessment of the RMSE, GMADL and Quantile loss functions with the Informer model to forecast future returns, subsequently using these forecasts to develop automated trading strategies. The research provides evidence that employing an Informer model trained with the GMADL loss function can result in superior trading outcomes compared to the buy-and-hold approach |
1 |
6 |
22 |
22 |
1 |
12 |
39 |
39 |
| The efficiency of various types of input layers of LSTM model in investment strategies on S&P500 index |
0 |
0 |
5 |
25 |
0 |
2 |
10 |
38 |
| The impact of the results of football matches on the stock prices of soccer clubs |
2 |
6 |
33 |
243 |
7 |
21 |
88 |
709 |
| The performance of time series forecasting based on classical and machine learning methods for S&P 500 index |
0 |
1 |
4 |
47 |
0 |
2 |
8 |
51 |
| The profitability of pairs trading strategies on Hong-Kong stock market: distance, cointegration, and correlation methods |
0 |
5 |
13 |
31 |
1 |
13 |
44 |
98 |
| The systemic risk approach based on implied and realized volatility |
0 |
0 |
2 |
19 |
0 |
0 |
10 |
27 |
| This study investigates the impact of investor sentiment on stock returns and trading volume, challenging the efficient market hypothesis. Using CRSP data from May 1998 to March 2022, methods like Fama-MacBeth and quantile regression were applied to analyze sentiment indicators such as the VIX, AAII Investor Sentiment Survey, Consumer Confidence, and Baker-Wurgler Index. The findings reveal that investor sentiment significantly influences stock returns and trading volume, especially during uncertain times. Sentiment also affects financial metrics like SMB, HML, RMW, and CMA uniquely. This research provides new insights and practical implications for investors and analysts, emphasizing the importance of considering sentiment in investment strategies to better anticipate market movements and manage risks |
3 |
7 |
10 |
10 |
5 |
12 |
24 |
24 |
| Value-at-risk — the comparison of state-of-the-art models on various assets |
0 |
0 |
6 |
101 |
0 |
2 |
27 |
168 |
| Variance Gamma Model in Hedging Vanilla and Exotic Options |
0 |
0 |
3 |
32 |
0 |
2 |
21 |
96 |
| Volatility as a new class of assets? The advantages of using volatility index futures in investment strategies |
0 |
0 |
0 |
49 |
0 |
0 |
1 |
134 |
| Which Option Pricing Model is the Best? High Frequency Data for Nikkei225 Index Options |
0 |
0 |
0 |
222 |
0 |
1 |
2 |
962 |
| Why you should not invest in mining endeavour? The efficiency of BTC mining under current market conditions |
0 |
0 |
1 |
16 |
0 |
0 |
3 |
104 |
| Total Working Papers |
29 |
90 |
455 |
3,792 |
75 |
263 |
1,257 |
11,255 |
LogEc is hosted by the
Örebro University School of Business.