Virgin Galactic (SPCE)

Total Page:16

File Type:pdf, Size:1020Kb

Virgin Galactic (SPCE) Valuentum Retail Equity Research Visit us at www.valuentum.com Ratings as of 10-Sep-2021 Data as of 10-Sep-2021 Virgin Galactic SPCE FAIRLY VALUED Buying Index™ 3 Value Rating Economic Castle Estimated Fair Value Fair Value Range Investment StyleSector Industry Unattractive $30.00 $15.00 - $45.00 MID-CAP BLEND Next Generation Disruptive Innovation Richard Branson, founder of Virgin Galactic, recently flew up to space in one Investment Considerations of the company's test flights, a milestone for the firm and the nascent private DCF Valuation FAIRLY VALUED space industry. Relative Valuation NEUTRAL Stock Chart (weekly) ValueCreation™ POOR ValueRisk™ VERY HIGH 2,000,000,000 57.00 ValueTrend™ NEGATIVE 52.00 Cash Flow Generation WEAK 1,500,000,000 47.00 Financial Leverage LOW 42.00 37.00 Growth AGGRESSIVE 1,000,000,000 32.00 Technical Evaluation BEARISH 27.00 Relative Strength WEAK 500,000,000 22.00 Money Flow Index (MFI) NEUTRAL 17.00 Upside/Downside Volume (U/D) BEARISH 0 12.00 Near-term Technical Resistance, 10-wk MA 27.00 DCF = Discounted Cash Flow; MFI, U/D = Please see glossary. MA = Moving Average Business Quality ValueCreation™ The week with the highest trading volume out of the last 30 weeks was a week of heavy selling, or distribution (red bar). ValueRisk™ Very Poor Poor Good Excellent Company Vitals Investment Highlights Low Market Cap (USD) $5,561 • Virgin Galactic is an aerospace entity that is Avg Weekly Vol (30 wks) 122,932 pioneering space flight, not just for astronauts, but for Medium 30-week Range (USD) 14.27 - 57.51 everyday private individuals like you and me. Its space Valuentum Sector Next Generation vehicles will allow customers a multi-day flight to High 5-week Return -0.8% experience weightlessness and view the Earth from space. The Virgin brand is a key asset when it may 13-week Return -30.0% come to capital raising. Very High 1 30-week Return -48.4% Firms that generate economic profits with little operating variability score near the top right of the matrix. Dividend Yield % 0.0% • Our fair value estimate range is huge because Virgin Dividends per Share 0.00 Galactic is a risky stock with hard-to-predict future Relative Valuation Forward P/E PEG Price / FV Forward Dividend Payout Ratio 0.0%forecasts. Operating costs to fly into space may be Monster Beverage 36.3 2.7 95.4% Est. Normal Diluted EPS 0.32uneconomical, and one tragedy similar to the Roku 720.3 NMF 97.3% Challenger disaster could derail the entire business. P/E on Est. Normal Diluted EPS 79.0 Wayfair 60.1 7.5 86.7% Est. Normal EBITDA 177 Zoom Video 64.5 3.5 83.0% • According to the US Chamber of Commerce, as Forward EV/EBITDA -15.1noted in Virgin Galactic's regulatory filings, 'the Peer Median 62.3 3.5 91.0% EV/Est. Normal EBITDA 27.5 commercial space market is expected to grow 6% per Virgin Galactic -17.8 NMF 84.6% Forward Revenue Growth (5-yr) 573.0% year, from $385 billion in 2017 to at least $1.5 trillion Price / FV = Current Stock Price divided by Estimated Fair Value Forward EPS Growth (5-yr) -212.6% by 2040, reaching 5% of US gross domestic product Financial Summary ----- Actual ----- Projected NMF = Not Meaningful; Est. = Estimated; FY = Fiscal Year (SPCE 10-K). The company is investing heavily, but Fiscal Year End: Dec-19 Dec-20 Dec-21 barriers to success are enormous. Returns Summary 3-year Historical Average Revenue 4 0 3 Return on Equity -154.5%• The market for commercial human spaceflight for Revenue, YoY% 32.7% -93.7% 1173.1% Return on Assets -60.6%private individuals is brand new, with fewer than 600 Operating Income -213 -275 -336 ROIC, with goodwill Negative ICpeople ever having traveled into space. Virgin Galactic Operating Margin % -5640.4% ######### -11100.9% ROIC, without goodwill Negative ICsees a huge opportunity for high net worth individuals Net Income -211 -273 -328 ROIC = Return on Invested Capital; NMF = Not Meaningful to provide a spaceflight experience. Tickets now cost Net Income Margin % -5576.9% ######### -10813.1% ~$450,000 each. Leverage, Coverage, and Liquidity Diluted EPS -1.08 -1.25 -1.42 In Millions of USD • Other companies are trying to do what Virgin Diluted EPS, YoY % 52.1% 14.8% 14.3% Total Debt 0Galactic is attempting. Blue Origin (private). SpaceX Free Cash Flow (CFO-capex) -229 -250 -361 Net Debt -679and Boeing are others. The current reality for Virgin Free Cash Flow Margin % -6044.0% ######### -11918.0% Total Debt/EBITDA 0.0 Galactic is that it continues to rack up losses and its In Millions of USD (except for per share items) future success is far from likely, in our view. Net Debt/EBITDA NMF MID-CAP EBITDA/Interest Excellent Structure of the Aerospace and Defense Industry GOOD Current Ratio 6.3 The global commercial aerospace duopoly is being challenged by encroaching international competitors who are intent on Quick Ratio NA increasing market share, but Boeing and Airbus continue to dominate the large commercial aircraft segment. Long-term NMF = Not Meaningful demand for commercial aircraft is cyclical and depends on the health of the credit markets, airline customers, and lessors, but massive backlogs and a strong multi-decade demand outlook are reasons for confidence. The defense industry has strong competition in all market segments and remains dependent on government funding decisions and competing budget priorities. The information and data contained in this report is not represented or warranted to be timely, complete, accurate, or correct. This report is for informational purposes only and should not be considered a solicitation to buy or sell a security. Before acting on any information in this report, you should consider whether the information is suitable for your particular circumstances and, if necessary, seek professional advice. Assumptions, opinions, and estimates are based on our judgment as of the date of the report and are subject to change without notice. Valuentum is not responsible for any errors or omissions or for results obtained from the use of this report. Redistribution is prohibited without written permission. To license Valuentum research, contact us at [email protected]. Page 1 Valuentum Retail Equity Research (10=best) Data as of 10-Sep-2021 Virgin Galactic SPCE FAIRLY VALUED Buying Index™ 3 Value Rating Economic Castle Estimated Fair Value Fair Value Range Investment Style Sector Industry Unattractive $30.00 $15.00 - $45.00 MID-CAP BLEND Next Generation Disruptive Innovation Economic Profit Analysis ValueCreation™ POOR Return on Invested Capital (ROIC) The best measure of a firm's ability to create value for shareholders is expressed by 1693.4% comparing its return on invested capital (ROIC) with its weighted average cost of 2000.0% capital (WACC). The gap or difference between ROIC and WACC is called the firm's 1500.0% economic profit spread. Virgin Galactic' 3-year average invested capital (without 1000.0% 766.6% goodwill) is negative, making it difficult to calculate a true economic profit spread. 558.7% However, earnings (EBI) last year were weak, indicating that the potential for economic 500.0% 124.7%159.9% profit creation is POOR for the company. In the chart to the right, we show the probable 195.2% path of ROIC in the years ahead based on the estimated volatility of key drivers behind 0.0% WACC, 11.8% the measure. The solid grey line reflects the most likely outcome, in our opinion, and -500.0% represents the scenario that results in our fair value estimate. -1000.0% ROIC - WACC Spread, 3-year historical average NMF -1500.0% ROIC - WACC Spread, 5-year projected average -549.8% These spreads equal the firm's annual average ROIC (excluding goodwill) less its WACC. -2000.0% ValueTrend™ NEGATIVE -2500.0% -3000.0% The firm's invested capital is often negative, and earnings last year declined from the -3500.0% year before, indicating deteriorating fundamentals. We expect ROIC (excluding goodwill) to be in the ballpark of about 160% by the end of our discrete forecast period. Weighted Average Cost of Capital (WACC) The graph above shows the firm's ROIC (excluding goodwill) compared with historical averages and its WACC. ROIC Calculation ---------- Actual ---------- Capital Structure 0.0% Equity Fiscal Year End: Dec-18 Dec-19 Dec-20 Debt Preferred Earnings before Interest Operating Income after Depreciation -167 -213 -275 - Adjusted Taxes (at 2% of EBIT) -3 -4 -6 + Amortization 0 0 0 100.0% + Non-cash Operating Items -13 4 0 - Minority Interest 0 0 0 Earnings before Interest -177 -205 -270 Cost of Equity Invested Capital Risk Free Rate Assumption4.3% Inventories - - - Fundamental Beta (ERP multiplier)1.2 + Receivables - - - Estimated Equity Risk Premium6.5% + Current Deferred Income Taxes - - - Cost of Equity Assumption 11.8% + Other Current Assets 38 44 49 + Property, Plant and Equipment, Net 34 49 53 After-tax Cost of Debt + Goodwill, Net (Cost in Excess) 0 19 20 Risk Free Rate Assumption4.3% + Intangibles - - - Synthetic Credit SpreadNA + Non Current Deferred Income Taxes - - - Cost of Debt AssumptionNA - Accounts Payable - - - Cash Tax Rate Assumption2.0% - Other Current Liabilities 106 116 115 After-tax Cost of Debt Assumption NA Invested Capital, with goodwill -32 -3 10 Cost of Preferred Stock Invested Capital, without goodwill -32 -22 -10 Preferred Dividends 0 Value of Preferred Stock 0 Return on Invested Capital, with goodwill 558.7% 1182.5% -7722.1% Cost of Preferred Assumption NA Return on Invested Capital, without goodwill 558.7% 766.6% 1693.4% In Millions of USD Weighted Average Cost of Capital (WACC) 11.8% Note: Valuentum may provide an adjusted ROIC measure to better reflect the economic substance ERP = Equity Risk Premium of a company's operations, as in the case of companies with negative invested capital.
Recommended publications
  • Investing with Volume Analysis
    Praise for Investing with Volume Analysis “Investing with Volume Analysis is a compelling read on the critical role that changing volume patterns play on predicting stock price movement. As buyers and sellers vie for dominance over price, volume analysis is a divining rod of profitable insight, helping to focus the serious investor on where profit can be realized and risk avoided.” —Walter A. Row, III, CFA, Vice President, Portfolio Manager, Eaton Vance Management “In Investing with Volume Analysis, Buff builds a strong case for giving more attention to volume. This book gives a broad overview of volume diagnostic measures and includes several references to academic studies underpinning the importance of volume analysis. Maybe most importantly, it gives insight into the Volume Price Confirmation Indicator (VPCI), an indicator Buff developed to more accurately gauge investor participation when moving averages reveal price trends. The reader will find out how to calculate the VPCI and how to use it to evaluate the health of existing trends.” —Dr. John Zietlow, D.B.A., CTP, Professor of Finance, Malone University (Canton, OH) “In Investing with Volume Analysis, the reader … should be prepared to discover a trove of new ground-breaking innovations and ideas for revolutionizing volume analysis. Whether it is his new Capital Weighted Volume, Trend Trust Indicator, or Anti-Volume Stop Loss method, Buff offers the reader new ideas and tools unavailable anywhere else.” —From the Foreword by Jerry E. Blythe, Market Analyst, President of Winthrop Associates, and Founder of Blythe Investment Counsel “Over the years, with all the advancements in computing power and analysis tools, one of the most important tools of analysis, volume, has been sadly neglected.
    [Show full text]
  • Stock Forecasters
    1 | P a g e 2008 SOFTWARE ENGINNERING OF WEB APPLICATIONS GROUP # 5 WEB BASED STOCK FORECASTERS This report describes the Stock Prediction System titled “TIMING THE MARKET” developed by our team which has various modules like Data Mining, Web Services, Neural Network Based Stock Prediction and Technical Indicators. SUBMITTED BY: AMARINDER CHEEMA ATEET VORA CHETAN JAIN PUNEET KATARIA RONAK SHAH SIDDHARTH WAGH 7 May, 2008 2 | P a g e ACKNOWLEDGEMENT It is a moment of great pleasure and satisfaction for us to express our sense of profound gratitude to all people who have contributed in making our project work a rich experience. We convey our sincere thanks Prof. Ivan Marsic for giving us an opportunity to take up the project work and also helping us in all phases of the project work and providing our team sound encouragement. We are also thankful to him for the technical guidance, help and facilities provided to us for successful completion of our project work. 3 | P a g e BREAKDOWN OF RESPONSIBILITIES 4 | P a g e TABLE OF CONTENTS Breakdown of Responsibilities.....................................................................................................03 Summary of Changes...................................................................................................................07 Glossary of Terms………………………………………………………………………......................08 1. Introduction..............................................................................................................................11 1.1 Project Goals and Requirements.................................................................................12
    [Show full text]
  • A Pairs Trading Strategy for GOOG/GOOGL Using Machine Learning
    A Pairs Trading Strategy for GOOG/GOOGL Using Machine Learning Jiayu Wu December 9, 2015 Abstract We apply the spread model, the O-U model and SVM to build a pairs trading strategy for GOOG/GOOGL. There are two parts of the project that we think are novel after reviewing past related work: first, we model not only price spread but also several selected technical indicators' \spread"; second, we use two new metrics for measuring our trading strategy instead of the traditional back-testing method because we want to focus more on future prediction rather than return prediction, and to achieve that, we also propose two algorithms to reconstruct the data set. We outline the process of how our strategy is executed and, at the end, show that our strategy delievers a good win-rate. 1 Introduction One important thing for a pairs trading strategy is to select a proper pair of financial instrucments. For exam- Pairs trading is a popular trading strategy in the last ple, if the price of security A always rises when the price three decades after it was first used by Morgan Stanley of security B rises, it seems that A and B may be used in 1980s. Pairs trading means to utilize a pair or a bag of for pairs trading. However, the explicit relation between related financial instruments to make profits by exploiting prices may not be good enough for a good pair. The good their relations. One important feature of pairs trading is pairs should share as many the same intrinsic character- that it is market-neutral, which is particularly appealing istics as possible.
    [Show full text]
  • Smart Money Flow Index Free
    Smart money flow index free click here to download The Smart Money Index (SMI), also called the Smart Money Flow Index by Bloomberg, is a technical indicator that is supposed to gauge “smart. The Smart Money Flow Index (SMFI) has long been one of the best kept secrets of Wall Street. Everybody knows the importance of a closing price and other last. Description: The Smart Money Flow Index was developed by WallStreetCourier in and is a trademark of www.doorway.ru Since then. From Wikipedia, the free encyclopedia. Jump to navigation Jump to search. Smart money index (SMI) or smart money flow index is a technical analysis indicator. The absolutely free educational site for technical traders and market The Smart Money Flow Index has long been one of the best kept secrets of Wall Street. That can be seen in the Smart Money Flow Index, which measures action . the North American Free Trade Agreement and a potential bilateral. Professional money managers were leery about buying stocks during the rebound Monday, judging from the Smart Money Flow Index, which. The Smart Money Flow Index (SMFI) is a leading-indicator in markets. That means when the SMFI drops sharply, usually the equity markets are. On Wall Street, there's what's considered the smart money and what's considered the dumb money. The dumb money are the ones who trade first thing in the. Money Flow Index measures trend strength and warns of likely reversal points. The only problem I have is that this SMFI index is not free of charge. The Smart Money Flow Index SMFI has long been one of the best kept.
    [Show full text]
  • Arxiv:1705.08022V1
    Using Macroeconomic Forecasts to Improve Mean Reverting Trading Strategies Yash Sharma Abstract A large class of trading strategies focus on opportunities offered by the yield curve. In partic- ular, a set of yield curve trading strategies are based on the view that the yield curve mean- reverts. Based on these strategies’ positive performance, a multiple pairs trading strategy on ma- jor currency pairs was implemented. To improve the algorithm’s performance, machine learning forecasts of a series of pertinent macroeconomic variables were factored in, by optimizing the weights of the trading signals. This resulted in a clear improvement in the APR over the evalua- tion period, demonstrating that macroeconomic indicators, not only technical indicators, should be considered in trading strategies. 1. Yield Curve The Yield Curve is a line that plots the interest rate, at a set point in time, of bonds having equal credit quality but differing maturity dates. The U.S. Treasury Yield Curve is used as a benchmark for other debt in the market, and can also be used to predict changes in economic output and growth. The shape of the yield curve gives an idea of future interest rate changes and economic activity. An inverted yield curve is often a harbinger of recession. A positively sloped yield curve is often a harbinger of inflationary growth. These predictive properties make the yield curve quite applicable in devising successful trading strategies. Many yield curve trading strategies are based on the conventional view that the yield curve mean-reverts to some historical norm. This market view is consistent with historical experience.
    [Show full text]
  • Quantitative Investment with Machine Learning in US Equity Market
    Advances in Economics, Business and Management Research, volume 68 International Symposium on Social Science and Management Innovation (SSMI 2018) Quantitative Investment with Machine Learning in US Equity Market Yuxiang Huang International School of Jiangxi University of Finance and Economics, Nanchang, China Abstract. Quantitative investment attempts to use computer algorithms to predict the price of securities and make automatic trading, in order to gain excess return on the stocks. This paper introduces a strategy based on machine learning algorithms and technical indicators. The model uses several popular technical indicators as inputs and predicts the movement of stock price after a certain short period. Then, a portfolio is constructed using the prediction result. The strategy buys the stocks whose returns exceed the predetermined threshold and sells (shorts) the stocks whose return are below the negative threshold. The empirical results show that the annual return is above 40%,which is far higher than the S&P500 index(2.14%). Considering the risk-adjusted return, the machine learning strategy is better than the S&P500 index. The Sharpe Ratio is higher than that of the S&P500. Keywords: Quantitative, machine learning, equity market. 1. Introduction One of the core issues of quantitative investment is to accurately predict the price changes of assets. If you can predict the rise and fall of asset prices, then investment managers can build a more effective portfolio to obtain greater investment returns. Under the traditional analyst-fund manager model, analysts predict the future price of assets through personal subjective analysis, but the model faces two challenges: First, the dramatic increase in the number of assets makes the traditional model to have significant implementation costs, which makes the model no longer applicable; secondly, the analyst's subjective judgment is influenced by his/her emotions and make its predictions no longer accurate.
    [Show full text]
  • Forex Investement and Security
    Investment and Securities Trading Simulation An Interactive Qualifying Project Report submitted to the Faculty of WORCESTER POLYTECHNIC INSTITUTE in partial fulfillment of the requirements for the Degree of Bachelor of Science by Jean Friend Diego Lugo Greg Mannke Date: May 1, 2011 Approved: Professor Hossein Hakim Abstract: Investing in the Foreign Exchange market, also known as the FOREX market, is extremely risky. Due to a high amount of people trying to invest in currency movements, just one unwatched position can result in a completely wiped out bank account. In order to prevent the loss of funds, a trading plan must be followed in order to gain a maximum profit in the market. This project complies a series of steps to become a successful FOREX trader, including setting stop losses, using indicators, and other types of research. 1 Acknowledgement: We would like to thank Hakim Hossein, Professor, Electrical & Computer Engineering Department, Worcester Polytechnic Institute for his guidance throughout the course of this project and his contributions to this project. 2 Table of Contents 1 Introduction .............................................................................................................................. 6 1.1 Introduction ....................................................................................................................... 6 1.2 Project Description ............................................................................................................. 9 2 Background ..................................................................................................................................
    [Show full text]
  • University of Vaasa Faculty of Business Studies Department of Accounting and Finance
    UNIVERSITY OF VAASA FACULTY OF BUSINESS STUDIES DEPARTMENT OF ACCOUNTING AND FINANCE Tuomo Tolonen AN EMPIRICAL TEST OF A 14-DAY MONEY FLOW INDEX AND RELATIVE STRENGTH INDEX HYBRID’S PREDICTIVE ABILITIES ON HELSINKI, OSLO AND STOCKHOLM STOCK EXCHANGES Master‟s Thesis in Accounting and Finance Finance VAASA 2011 1 TABLE OF CONTENTS page LIST OF TABLES 5 LIST OF FIGURES 5 ABSTRACT 7 1. INTRODUCTION 9 1.1 Purpose of the study 10 1.2 Structure of the study 11 1.3 Limitations of the study 11 2. MARKET EFFICIENCY AND RANDOM WALK 13 2.1 Behavioral finance 13 2.2 Three forms of market efficiency 14 2.2.1 Weak form tests 14 2.2.2 Semi-strong form tests 15 2.2.3 Strong form tests 15 3. THEORETICAL FOUNDATIONS AND UTILIZATIONS OF TECHNICAL ANALYSIS 16 3.1 Momentum 17 3.2 Trading volume 19 3.3 Volatility 20 3.4 Technical analysis as a complement of fundamental valuation 21 3.5 Data snooping 22 4. TECHNICAL INDICATORS 24 4.1 Momentum indicators 24 4.2 Oscillators 25 4.3 Visual pattern analysis 25 4.4 MFI-RSI hybrid 25 4.4.1 Relative strength index 25 4.4.2 Money flow index 26 4.4.3 MFI and RSI trading methods 27 4.4.4 MFI-RSI hybrid construction and trading rules 28 2 3 5. DATA AND METHODOLOGY 30 5.1 Market data and sample selection 30 5.1.1 Descriptive statistics 31 5.2 Hypothesis 34 5.3 Methods 35 5.3.1 Sharpe ratio 35 5.3.2 Sortino ratio 35 5.3.3 Kolmogorov-Smirnov test for distribution normality 35 5.3.4 Mann-Whitney rank-sum test for statistical significance 37 5.3.5 T-test for testing the long positions against short positions 37 5.3.6 Simple regression model for testing the risk ratio dependence on MFI-RSI trading bound values 38 6.
    [Show full text]
  • Is Investor Sentiment Contagious? International Sentiment and UK Equity Returns
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by LJMU Research Online Presented at the PFMC-13 Conference; Revised Draft for Journal of Behavioral and Experimental Finance Is investor sentiment contagious? International sentiment and UK equity returns July 2014 Provisional Draft: Not for Quotation Abstract This paper contributes to a growing body of literature studying investor sentiment. Separate sentiment measures for UK investors and UK institutional investors are constructed from commonly cited sentiment indicators using the first principle component method. We then examine if the sentiment measures can help predict UK equity returns, distinguishing between “turbulent” and “tranquil” periods in the financial markets. We find that sentiment tends to be a more important determinant of returns in the run-up to a crisis than at other times. We also examine if US investor sentiment can help predict UK equity returns, and find that US investor sentiment is highly significant in explaining the UK equity returns. Keywords: Investor sentiment; contagion; institutional investors; equity returns JEL Classification: G02, G12, G14 1 1. Introduction We investigate the influence on UK equity returns of foreign and local components of investor sentiment, using measures of sentiment for the UK and US. The objectives of the paper are two. First we construct two new measures of investor sentiment for the UK at a weekly frequency, distinguishing between “market” and “institutional” sentiment, on the grounds that financial institutions may be expected to be better-informed about the stock market than other investors. Institutions may therefore develop sentiment about stocks in different ways from the market in general, for example: perhaps more rapidly or simply using different information sets.
    [Show full text]
  • Be Financially Secure: Predicting Auto Stock Prices with Past Data
    Be Financially Secure: Predicting Auto Stock Prices with Past Data Rutvik Parikh Massachusetts Academy of Math & Science at WPI [email protected] Table of Contents Abstract 1 Introduction 1 Methods and Materials 3 Moving Average 3 Relative Strength Index (RSI) 4 Money Flow Index (MFI) 4 Rate of Change Indicator (ROC) 5 Models 5 Results 6 Discussion 9 References 13 Parikh 1 1. Abstract Deciding which auto stocks to invest in can be overwhelming and frightening for many individuals due to the great amount of past data available, and the ambiguity of its effects on future outcomes. Therefore, the goal of this project is to develop a model for auto stock price prediction using technical analysis based on multiple indicators. First, a few quantitative indicators were chosen to assist with prediction. Next, computer algorithms were used to calculate numerical values of these indicators using stock price data. These values were then employed to predict whether the closing price of a particular auto stock would rise or fall in the short term; distinct subsets of these indicators were tested in an effort to obtain the strongest and most helpful model. These models were tested on stock price data from several major auto companies, including General Motors, Subaru, and Ford. This data was from 2013 to 2018. The accuracies of the models were spread out over a range of 42% to 69%. The most successful model relied on the rate of change indicator, suggesting that this indicator may have been the most applicable to the auto industry during the aforementioned years. The performance of the models shows that technical analysis effectively gauges the future behavior of auto stocks.
    [Show full text]
  • Commodity Future Money Flows Trading Strategies Based on HMM
    International Journal of Statistics and Probability; Vol. 6, No. 4; July 2017 ISSN 1927-7032 E-ISSN 1927-7040 Published by Canadian Center of Science and Education Commodity Future Money Flows Trading Strategies Based on HMM Jishan Ma1 & Yuanbiao Zhang2 1 Finance Department of International Business School, Jinan University, Zhuhai, China 2 Electrical And Information College, Jinan University, Zhuhai, China Correspondence: Jishan Ma, Jinan University, No.206, Qianshan Road, Xiangzhou District, Zhuhai, China. E-mail: [email protected] Received: May 4, 2017 Accepted: May 24, 2017 Online Published: June 7, 2017 doi:10.5539/ijsp.v6n4p16 URL: https://doi.org/10.5539/ijsp.v6n4p16 Abstract This paper aims to establish a quantitative trading strategy of commodity futures based on market money flows. Firstly, we use Accumulation/Distribution index to respectively construct the CMF index which represents the ratio of total capital flows to total volume, and the CHO index which represents the exponential moving average of the cumulative capital flows. In view of the different flows of money between buyers and sellers, the establishment of the transaction net volume index VTL is used to describe respectively the flow of money between buyers and sellers. On this basis, the HMM model is introduced, and the above three kinds of indexes are combined to choose the time, at which we execute the stop-loss operation and risk control. Finally, all performance index values of the strategy are as follows: the rate of initial capital return is 193.77%, the annual rate of return is 99.86%, the maximum retracement rate is 15.73%, the Sharpe rate is 2.05 and the price earnings ratio is 4.01.
    [Show full text]
  • Juxtapositional Analysis of Money Flow Vs. Momentum Indicator
    CALIFORNIA STATE SCIENCE FAIR 2004 PROJECT SUMMARY Name(s) Project Number Nimi P. Katragadda S1213 Project Title Are You Making Money in the Stock Market? Juxtapositional Analysis of Money Flow vs. Momentum Indicator Abstract Objectives/Goals Several different market indicators are often used to predict trends or patterns in stocks. Two of the most common tools used by stock analysts are money flow and momentum. The Money Flow Index ("MFI") is a momentum indicator that measures the strength of money flowing in and out of a security. It is related to the Relative Strength Index, but the Money Flow Index accounts for volume. The Momentum indicator measures the amount that a security's price has changed over a given time span. In this project, these two different indicators were used as tools for deducing patterns. The purpose of this project was to, first, discover if both the Money Flow Index and the Momentum indicator produced credible predictions; and second, to deduce, in terms of percentage, whether the Money Flow Index or the Momentum indicator was more beneficial. Methods/Materials The Money Flow Index can be interpreted by finding a divergence between the indicator and the price action. If the price trends higher and the MFI trends lower (or vice versa), a reversal may be imminent. The Momentum indicator can often be used as a leading indicator. As a market peaks, the Momentum indicator will climb sharply and then fall off-- diverging from the continued upward or sideways movement of the price. Similarly, at a market bottom, Momentum will drop sharply and then begin to climb well ahead of prices.
    [Show full text]