Hedging Properties of Algorithmic Investment Strategies Using Long Short-Term Memory and Time Series Models for Equity Indices

This paper proposes a novel approach to hedging portfolios of risky assets when financial markets are affected by financial turmoils. We introduce a novel approach to diversification on the level of ensemble algorithmic investment strategies (AIS) built on the prices of these assets. We employ four types of diverse models (LSTM, ARIMA-GARCH, momentum, contrarian) to generate price forecasts, which are used to produce investment signals in single and complex AIS. We verify the diversification potential of different types of investment strategies consisting of various assets classes in hedging ensemble AIS built for equity indices (S&P 500). Our conclusion is that LSTM-based strategies outperform the other models and that the best diversifier for the AIS built for the S&P 500 index is the AIS built for Bitcoin. Finally, we test the LSTM model for 1-hour frequency of data. We conclude that it outperforms the results obtained using daily data

Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies

This paper investigates the issue of an adequate loss function in the optimization of machine learning models used in the forecasting of financial time series for the purpose of algorithmic investment strategies (AIS) construction. We propose the Mean Absolute Directional Loss (MADL) function, solving important problems of classical forecast error functions in extracting information from forecasts to create efficient buy/sell signals in algorithmic investment strategies. Finally, based on the data from two different asset classes (cryptocurrencies: Bitcoin and commodities: Crude Oil), we show that the new loss function enables us to select better hyperparameters for the LSTM model and obtain more efficient investment strategies, with regard to risk-adjusted return metrics on the out-of-sample data.Comment: 12 pages, 6 figure

Which Option Pricing Model is the Best? High Frequency Data for Nikkei225 Index Options

Option pricing models are the main subject of many research papers prepared both in academia and financial industry. Using high-frequency data for Nikkei225 index options, we check the properties of option pricing models with different assumptions concerning the volatility process (historical, realized, implied, stochastic or based on GARCH model). In order to relax the continuous dividend payout assumption, we use the Black model for pricing options on futures, instead of the Black-Scholes-Merton model. The results are presented separately for 5 classes of moneyness ratio and 5 classes of time to maturity in order to show some patterns in option pricing and to check the robustness of our results. The Black model with implied volatility (BIV) comes out as the best one. Highest average pricing errors we obtain for the Black model with realized volatility (BRV). As a result, we do not see any additional gain from using more complex and time-consuming models (SV and GARCH models. Additionally, we describe liquidity of the Nikkei225 option pricing market and try to compare our results with a detailed study for the emerging market of WIG20 index options (Kokoszczyński et al. 2010b).option pricing models, financial market volatility, high-frequency financial data, midquotes data, transactional data, realized volatility, implied volatility, stochastic volatility, microstructure bias, emerging markets

Does Historical VIX Term Structure Contain Valuable Information for Predicting VIX Futures?

We suggest that the term structure of VIX futures shows a clear pattern of dependence on the current level of VIX index. At the low levels of VIX (below 20), the term structure is highly upward sloping, while at the high VIX levels (over 30) it is strongly downward sloping. We use these features to predict future VIX futures prices more precisely. We begin by introducing some quantitative measures of volatility term structure (VTS) and volatility risk premium (VRP). We use them further to estimate the distance between the actual value and the fair (model) value of the VTS. We find that this distance has significant predictive power for volatility futures and index futures and we use this feature to design simple strategies to invest in VIX futures

Improvement of calf behaviour and veal quality using rearing at foster cows

The experiment was carried out at the ecological farm Ekofarma Kaszubska in Poland. Ten Holstein-Friesian bulls of the same age were selected for the experiment and divided into two experimental groups. Five calves were raised in a pen without access to their mothers. They were taken with whole milk served from a bucket equipped with a teat. The remaining five bulls were reared at two foster cows. The rearing lasted six months, after which the animals were slaughtered. During the rearing, behavioural observations of each of the experimental groups were carried out every month for 3 hours. During the observation, the number of ‘licking cases’ of the pen equipment or other calf was counted. The study was conducted in three identical replications one after the other. The collected experimental data were statistically analysed using IBM SPSS Statistics. It was found that calves reared with suckler cows gained weight faster and were characterised by better muscle class (assessed in the EUROP system) compared to the control group. Behavioural assessments showed less adverse behaviours, such as licking other calves or pen equipment, in the suckling group compared to the control group. This difference was most evident in relation to calves up to 3 months of age. No significant differences were found between the groups of older calves. This is due to the increased need for calves to suck in the first weeks of life. Strong urine drinking tendencies were demonstrated in both groups, so this type of behaviour would not be related to the rearing system, but rather to mineral deficiencies in the diet. Based on the obtained results, it can be concluded that the increased availability of milk, and the frequent natural intake of milk directly from the udder, have a positive effect on calves’ growth and well-being

Use of somatic cell count as an indicator of colostrum quality

The impact of cow mammary gland diseases on the quality of colostrum is not conclusively defined; research results are conflicting. However, it is widely believed that mastitis lowers the level of immunoglobulins and the quality of the colostrum produced. Therefore, the aim of this study was to determine the influence of somatic cell counts (SCC) on the colostrum immuno-stimulating and chemical components. The experiment was conducted on an experimental organic dairy farm in which a herd of approximately 250 cows was kept in a freestall housing system, with the average performance exceeding 6,000 kg of milk per lactation. Colostrum and milk samples were taken individually from each cow seven times during the experiment: from the first to second day after calving–twice per day, and from the third to fifth day after calving–once per day. Therefore, after preliminary analyses, the cows were divided into two groups based on the cytological quality of their colostrum at the first collection: 1. SCC �400,000 cells/ml (good quality colostrum; GCC– 18 cows), 2. SCC � 400,000 cells/ml (low quality colostrum; LCC– 22 cows). The study found almost double the concentration of immunoglobulins and essential fatty acids in first milking colostrum in the GCC group than in colostrum from the LCC group. In addition, an increase in the concentration of lysozyme in first milking colostrum was associated with a decrease in the concentration of immunoglobulins. In addition, the increase in the level of lysozyme was associated with a decrease in the concentration of immunoglobulins. In conclusion, the SCC of first milking colostrum can be used as an indicator of colostrum quality

Age of cows, as a factor shaping the level of immunostimulating properties of colostrum

A characteristic feature of the organic system of production is the lower productivity of cows. The aim of the study was to determine the effect of cow age and collecting number on the level of immunostimulating colostrum components in an organic farm. From the basic herd specializing in organic milk production, 40 cows of the Polish Holstein-Friesian Black and White breed were selected: 10 primiparous, 10 cows in 2nd lactation, 10 cows in 4th lactation and 10 cows in 5th lactation. The colostrum samples were collected according to the following scheme: the first one maximum of two hours after calving, the second on the same day and the third and fourth on the following day. For three consecutive days samples were taken once a day (7 colostrum samples from each cow). Statistically significant differences in the level of bioactive components of colostrum with immunostimulating properties has been shown due to the time of intake from calving and significant differences in the level of these components due to the age of cows. Multiparous cows synthetized colostrum with a higher content of total protein, casein and non-fat dry matter than the primiparous. Variability of the immunoglobulin content of colostrum obtained in 1st and in the 2nd collecting after calving was higher in multiparous cows than in primiparous cows. Furthermore, it has been shown that there was a clear correlation between the quality of colostrum and the age of cows. In conclusion, a high impact of the interaction of age of cows x collecting number on the development of colostrum stimulating ingredients in the organic production system has been demonstrated

Interaction between the level of immunoglobulins and number of somatic cells as a factor shaping the immunomodulating properties of colostrum

The aim of this study was to investigate the association between immunoglobulins and SCC as a factor in shaping the content of the immunostimulatory components of colostrum. Seventy-eight multiparous Polish Holstein–Friesian cows were selected for the experiment. Colostrum samples were collected immediately after calving (up to a max. of 2 h). The cows were divided into groups according to the following levels: Immunoglobulins (IG class)—(IG1) over 50 g/L, (IG2) up to 50 g/L; SCC class—(SCC1) up to 400 000/ml, (SCC2) 400–800 000/ml, (SCC3) over 800 000/ml. Colostrum assigned to the IG1 SCC1 group had a statistically significant higher (p ≤ 0.01) concentration of both whey proteins and fatty acids compared to the IG1 SCC2 and SCC3 groups. The concentration of IgG, IgM, and IgA was shown to be higher in IG1 SCC1 than IG2 SCC3 by 226%, 149%, and 115%, respectively. The concentration of lactoferrin was shown to be higher in IG1 SCC1 than IG2 SCC3 by 149%. The determination of colostrum quality based on the concentration of immunoglobulins in the colostrum may not be sufficient because serum IgG concentrations at birth show a linear increase relative to colostrum SCC. A breakdown of colostrum into quality classes, taking into account the level of SCC, should therefore be introduced