Short-Term Economic Forecasting by Complex-Valued Autoregressions

One of the directions that can expand the instrumental base for modeling the economy is complex-valued economics – ​a section of economic and mathematical modeling devoted to the use of models and methods of the theory of the function of a complex variable in economics. The article discusses the possibility of short-term economic forecasting using autoregressive models of complex variables. A classification of possible modifications of complex-valued autoregressive models is given, and the main properties of each of the classes of these models are shown. One of the varieties of these complex-valued models uses current and past errors of approximation, which means that it can be compared with the widely used model of autoregressive real variables ARIMA(p, d, q). The article makes such a comparison, both on a theoretical level and on a practical example.

Econometric approaches to forecasting financial support of socio-economic development of the region

In the given article it is noted that the level of forecasting of processes of social development is determined by the efficiency of planning and management of economy and other spheres. Social and economic forecasting of basic trends of social development allows use of special calculation and logic methods, giving the opportunity to determine parameters of functioning of separate elements of productive forces in their interrelation and interdependence. At the current stage of regional development of the state, the forecasting of the management of social-economic processes in the region is urgent, and the need for their improvement in order to obtain effective tools for determining the main guidelines and directions of regional policy. Predictions that include scientific justification should be central to the planned decisions of state authorities and the implementation of social-economic policies in the region, to determine the main directions of its future development, place and role in the national economy. The process of forming a modern system of forecasting regional development in Ukraine took place under conditions of a large-scale state transformation and reorganization. The change in the political regime and reform of the Ukrainian economy, which began in the 1990s, led to the inversion of the role of the territory in the system of public administration. Regions that previously had very limited rights in the agricultural sector, received the right to make political, economic, social, cultural and other decisions on their own. Economic forecasting is necessary for determining ways of society development and economic resources which provide its achievement, for revealing most likely and economically efficient variants of long-term, medium term and current plans, grounding main directions of economic and technical politics, forecasting the consequences of the made decisions and measures taken at present. Application of econometric models in economics gives the opportunity to distinguish and formally describe the most significant, the most essential relations of economic variables and objects, as well as to get new knowledge about the object in the inductive way. In such model, in the simplified form, by many assumptions, the main dependence between economic indicators is determined.

An Economic Forecasting Method Based on the LightGBM-Optimized LSTM and Time-Series Model

Stock price prediction is very important in financial decision-making, and it is also the most difficult part of economic forecasting. The factors affecting stock prices are complex and changeable, and stock price fluctuations have a certain degree of randomness. If we can accurately predict stock prices, regulatory authorities can conduct reasonable supervision of the stock market and provide investors with valuable investment decision-making information. As we know, the LSTM (Long Short-Term Memory) algorithm is mainly used in large-scale data mining competitions, but it has not yet been used to predict the stock market. Therefore, this article uses this algorithm to predict the closing price of stocks. As an emerging research field, LSTM is superior to traditional time-series models and machine learning models and is suitable for stock market analysis and forecasting. However, the general LSTM model has some shortcomings, so this paper designs a LightGBM-optimized LSTM to realize short-term stock price forecasting. In order to verify its effectiveness compared with other deep network models such as RNN (Recurrent Neural Network) and GRU (Gated Recurrent Unit), the LightGBM-LSTM, RNN, and GRU are respectively used to predict the Shanghai and Shenzhen 300 indexes. Experimental results show that the LightGBM-LSTM has the highest prediction accuracy and the best ability to track stock index price trends, and its effect is better than the GRU and RNN algorithms.

Conceptualization of Machine Learning in Economic Forecasting

Economic forecasting is a very important aspect that policymakers in the financial and corporate organization rely on because helps them to determine future events that might infringe some hardship on the economy and the citizens at large. However, the principal statistical pointers that are available to the public domain provide numerous reservations and doubts for their economics estimates as it is later released with frequent issues to major revisions and also it shows a great lag in decision making for an incoming event. To this effect, the expansion of the latest forecasting patterns was important to address the gaps. Hence, this paper examines the conceptualization of Machine learning in economic forecasting. To achieve this, the Italian economy was used as the dataset, and machine learning controlled tools were used as the method of analysis. The result obtained from this study shows that machine learning is a better model to use in economic forecasting for quick and reliable data to avert future events.

The Firm as an Engine of Imagination: Organizational prospection and the making of economic futures

What do organizations do, and why? An important but only selectively scrutinized aspect of the doings of organizations consists in their creation of imaginaries of economic futures. Under conditions of uncertainty, it is through ‘imagined futures’ that organizations motivate and find the rationale for their decisions, coordinate activities, manage stakeholders and compete with one another. This article suggests making the construction of imagined futures a vantage point for the study of organizations and processes of organizing. It focuses on ‘instruments of imagination’ used by firms to create ‘fictional expectations’ which are used to come to terms with an uncertain future – and to proactively shape this future. Instruments discussed here include strategic planning, technological projections, economic forecasting, and business plans among others. The article argues that a fruitful general analytical perspective can be developed by bringing the constitution, contestation and effects of imagined futures to the forefront of organizational analysis.