TY - THES

T1 - Leading indicators in macroeconomics to better understand business cycles and predict commodity trading

AU - Oberschmidleitner, Patrick

N1 - no embargo

PY - 2023

Y1 - 2023

N2 - The objective of this thesis was to assess, develop and implement a forecast model based on macroeconomic and technical indicators, to predict business cycle changes before their occurrence to better understand business cycles and predict commodity trading, with a specific focus on the short- and medium-term cycles of the commodities of oil, copper, and uranium. The theoretical foundation of business cycles for a better understanding of cyclic movements is covered. Business cycles are then analyzed and divided into long-, medium-, and short-term business cycles according to Kondratieff, Schumpeter, Juglar, Kuznets, and Kitchin. It highlights the importance of understanding and predicting these cycles and explains how they affect investors, policymakers, researchers, and corporations. An introductory insight into the commodity market and commodity trading process is provided and afterwards, economic cycles in commodity markets of oil, copper and uranium are evaluated. The thesis then provides an extensive overview of technical and economic indicators with a first evaluation of their leading ability. Additionally, influencing economic factors of commodities are identified and evaluated. The theoretical findings are then applied in a built model, which ranks each analyzed indicator on its predictive ability and evaluates which indicators correlate in a suitable way with each other to combine them and use them for price movement forecasting. Therefore, the indicators are applied to available price chart data from past events and reversal points to gain proof of applicability. The proposed model evaluates 18 different indicators of which 8 are technical and 10 are economic indicators. Via a correlation matrix the correlation between different indicators will be evaluated to identify highly correlating indicators with leading tendency. The outcome shows that for oil and copper a combination of RSI, CCI, and BB indicators show the most promising results. For uranium a combination of RSI, SO, and W%R indicators show the most promising result. At the end a price outlook for oil, copper, and uranium is carried out which concludes that energy prices are on an upward trend in the long term.

AB - The objective of this thesis was to assess, develop and implement a forecast model based on macroeconomic and technical indicators, to predict business cycle changes before their occurrence to better understand business cycles and predict commodity trading, with a specific focus on the short- and medium-term cycles of the commodities of oil, copper, and uranium. The theoretical foundation of business cycles for a better understanding of cyclic movements is covered. Business cycles are then analyzed and divided into long-, medium-, and short-term business cycles according to Kondratieff, Schumpeter, Juglar, Kuznets, and Kitchin. It highlights the importance of understanding and predicting these cycles and explains how they affect investors, policymakers, researchers, and corporations. An introductory insight into the commodity market and commodity trading process is provided and afterwards, economic cycles in commodity markets of oil, copper and uranium are evaluated. The thesis then provides an extensive overview of technical and economic indicators with a first evaluation of their leading ability. Additionally, influencing economic factors of commodities are identified and evaluated. The theoretical findings are then applied in a built model, which ranks each analyzed indicator on its predictive ability and evaluates which indicators correlate in a suitable way with each other to combine them and use them for price movement forecasting. Therefore, the indicators are applied to available price chart data from past events and reversal points to gain proof of applicability. The proposed model evaluates 18 different indicators of which 8 are technical and 10 are economic indicators. Via a correlation matrix the correlation between different indicators will be evaluated to identify highly correlating indicators with leading tendency. The outcome shows that for oil and copper a combination of RSI, CCI, and BB indicators show the most promising results. For uranium a combination of RSI, SO, and W%R indicators show the most promising result. At the end a price outlook for oil, copper, and uranium is carried out which concludes that energy prices are on an upward trend in the long term.

KW - Leading Indicators

KW - Commodity Price Prediction

KW - Commodities

KW - Oil

KW - Copper

KW - Uranium

KW - Business Cycles

KW - Technische Indikatoren

KW - Wirtschaftliche Indikatoren

KW - Leading Indicators

KW - Commodity Price Prediction

KW - Commodities

KW - Oil

KW - Copper

KW - Uranium

KW - Business Cycles

KW - Technical Indicators

KW - Economic Indicators

KW - Price Trend Reversal

U2 - 10.34901/mul.pub.2023.233

DO - 10.34901/mul.pub.2023.233

M3 - Master's Thesis

ER -