FORECASTING - FinMan.pdf

FINANCIAL MANAGEMENT PRESENTATION FINANCIAL FORECASTING PREPARED BY: BSACC 3A - GROUP 2 FORECASTING 01 A projection of future sales, revenues, earnings, costs, and other possible variables that are helpful in the firm's operations. 02...

FINANCIAL MANAGEMENT PRESENTATION FINANCIAL FORECASTING PREPARED BY: BSACC 3A - GROUP 2 FORECASTING 01 A projection of future sales, revenues, earnings, costs, and other possible variables that are helpful in the firm's operations. 02 It is the starting point of business planning, making it as one of the most important factors to be applied to business. Primary Objective: Reduce risks and uncertainty that the firm will face in making a decision. Financial forecasting is essential for stability, growth, and the overall success of businesses by providing a roadmap for future financial health. IMPORTANCE Forecasts serve as Helps businesses or individuals benchmarks against which plan for the future. actual performance can be Companies can identify potential measured. This allows risks and develop strategies to organizations to evaluate mitigate them. their progress and make Foundation for creating budgets. adjustments as needed. Determine their Investors and stakeholders funding needs, rely on financial forecasts to whether it’s securing assess the potential returns loans, issuing bonds, and risks of investing in a or raising equity. company. USERS OF FORECAST Forecast, with its wide array of application, is used by people within and outside the company for various reasons. Some of the users of forecast and their purposes are listed below: Top Management makes use of the forecast as a tool for long- range planning, particularly in providing a basis for performance targets, implementing long-range strategic objectives, and making capital budgeting decisions. USERS OF FORECAST Production Manager utilizes the forecast to determine the amount of raw materials that will be needed in the production, the budget, schedule of production activities, inventory levels to maintain so as not to disrupt the production, labor hours, and the schedule of shipments. Purchasing Manager uses the forecast to ascertain the volume or bulk of materials that should be purchased for a particular period. This avoids overstocking or understocking of inventories. USERS OF FORECAST Finance Manager uses the forecast to anticipate the funding needed by the firm. The finance manager must establish the firm's cash inflows and outflows, and indicate the exact moment when the firm will need additional funding. Marketing Manager makes use of the forecast to estimate how much sales should be made in a particular period, and to plan promotional and advertising activities for the products. USERS OF FORECAST Human Resource Manager utilizes the forecast to supply the human resource needed in achieving the firm's objectives. He or she must specify when to hire additional people to support the firmis operations. Colleges and Universities makes use of the forecast to estimate how much sales makes use of forecast to identify possible enrollees in a school year. The figures on hand help determine the revenues to be obtained from the tuition fees, the faculty to be hired, the planning of room assignments, and building of facilities. FORECASTING APPROACHES In general, there are two approaches to forecasting: Qualitative Approach Quantitative Approach QUALITATIVE FORECASTS used for short-term forecasts incorporate factors: decision-maker's Important point intuition emotion predicts future outcomes personal experiences based on subjective judgment value system numerical data is insufficient and irrelevant judgment-based insights are weighs more CORE PRINCIPLES OF QUALITATIVE FORECASTING METHODS Expert Opinions Delphi Method Sales Force Polling Consumer Market Opinions PERT-derived forecasts  EXPERT OPINION Under this method, the views of the managers or a group with a high level of expertise, often in combination with statistical models, are synthesized to generate a consensual forecast. 01 simple and easy to implement used in conjunction with a 02 quantitative method 03 BUT, there may be a presence of bias.  a process used to arrive at a DELPHI group opinion or decision by surveying a panel of experts METHOD Experts respond to several rounds of questionnaires, and the responses are aggregated and shared with the group after each round. Members are asked individually to avoid peer pressure or group consensus  SALES FORCE POLLING Involves gathering sales predictions directly from sales team, who are closest to the customers and the market MAIN STRENGTH: First-hand Insights PURPOSE: Leverages frontline insights to forecast future sales based on customer interactions and market trends.  CONSUMER MARKET SURVEY involves collecting data from customers about their preferences, buying behaviors, and intentions to purchase PURPOSE: Understands MAIN ADVANTAGE: consumer demand directly Direct Customer Feedback from the source to inform product planning and marketing strategies. QUANTITATIVE FORECASTING The two types of quantitative forecasting are: Associative or Causal Time Series Forecasting Models Uses a variety of mathematical models that rely on historical data and/or causal variables to forecast demand. TIME SERIES FORECASTING Models Mod Naive Model Moving Average Weighted Moving Average Important point Exponential smoothing Assumes that the future is a function of the past. Trend Projections Historical data are used to predict the future using sequences with equal periods. Other variables, no matter how significant, are ignored. DECOMPOSITION OF A TIME SERIES FORECAST Four components of a time series: Trend Seasonality Cycle Random Variations Analyzing a time series means breaking down past data into components and then projecting them forward.  NAIVE MODEL (BENCHMARK MODEL): Estimating technique in which the last period's actuals are used as this period's forecast, without adjusting them or attempting to establish causal factors. It is used only for comparison with the forecasts generated by the better techniques. 01 One of the simplest time series forecasting techniques. 02 It assumes that the most recent observation is the best Important point predictor for the next observation. 03 This method is particularly useful as a baseline forecast The naive model operates under to compare more complex models against. the assumption of continuity or stability in the time series 04 It serves as a useful tool for quick predictions ADVANTAGES 01 It is cheap to develop 02 It does not require any software or machine. 02 Storing of data is simple. 02 It is very easy to operate. LIMITATIONS It’s generally only accurate for short-term predictions. It does not attempt to explain casual relationships with A drastic change in the the forecasted variable for forecasting is not variable. captured. Thus, this method is ineffective for time series data with strong trends, seasonality, or cyclical patterns. EXAMPLE Formula: WHERE: Y’t+1 = Forecasted value for the next period Yt = Observed value for this period STEPS: 1. Identify the most recent data point. 2. Set the forecast for the next period. MOVING AVERAGE Formula: simplest among the time series model In this model, the number of period (n), in which a series of averages will be created and computed, should be decided. WHERE: The most appropriate number of periods n = number of observations that would result in the smallest MA t+1 = moving average for the forecasting error should be determined. next period The most recent observations are used to Dt = current period calculate the average to obtain the forecast Dt-1= previous period for the next period.  EXAMPLE Formula: STEPS: (1) Choose the number of periods. (2) Calculate the moving average. (3) Compare forecasts. (4) Adjust and refine. EXAMPLE OBSERVATION: Trend Increase April to October Trend Decrease November to December Advantages Simplicity Adaptability Effective for Stable Data Useful for short-term forecasting Disadvantages Requires numerous records and data inconvenient to update the records and data needed to conduct a forecast Limited for long-term forecasting  WEIGHTED MOVING AVERAGE A weighted moving average (WMA) is more powerful and economical compared with moving average. This method is giving heavier weight to the most recent month and providing a more accurate projection rather than giving equal qeight for all the periods under observation. The future is more dependent on the recent past than on the distant past.  EXAMPLE Formula: STEPS: (1) Select the period to compute (2) Solve the weighted moving average of previous period (3) Compare forecasts (4) Adjust and refine EXAMPLE OBSERVATION: Trend Increase April to July LIMITATIONS It is less sensitive to real changes as the period under observation increases. It does not pick up trends very well It requires extensive records of data  EXPONENTIAL SMOOTHING This method uses the weighted moving average technique where more weight is given to the recent data. It is supported by the belief that the future is more dependent on the recent past than on the distant past. Exponential smoothing is a popular technique that does not involve voluminous records to forecast, and is easy to use and effective for short-run forecasting. The method is known to be useful on random historical data with no seasonal fluctuations. One disadvantage of the method, however, is that it assumes that changes in the mean of the time series is slow, making the model underestimate the data when there is sudden fluctuation.  EXPONENTIAL SMOOTHING KEY FEATURES Weighted Moving Average Short-Term Forecasting No Seasonality  EXPONENTIAL SMOOTHING ADVANTAGES: Simplicity Minimal Data Requirements Effectiveness DISADVANTAGES: Slow Response to Sudden Changes Limited Use for Trend or Seasonality  EXPONENTIAL SMOOTHING How It Works? The method uses a smoothing factor α (0 < α < 1), which determines how much weight is given to the most recent data. Higher values of α make the forecast more sensitive to recent changes in the data, while lower values make it more stable but slower to respond to changes.  EXPONENTIAL SMOOTHING FORMULA:  EXPONENTIAL SMOOTHING FORMULA:  EXPONENTIAL SMOOTHING SIMPLE PROBLEM You are given the actual sales data for 5 time periods: Time Period 1: 50 Time Period 2: 55 Time Period 3: 48 Time Period 4: 52 Time Period 5: 60 Assume that the initial forecast (F1​) for Time Period 1 is 50 (i.e., the forecast starts from the first actual value), and the smoothing factor (α) is 0.6. Task: Use exponential smoothing to forecast the values for Time Periods 2 through 6. EXPONENTIAL SMOOTHING BOOK PROBLEM EXPONENTIAL SMOOTHING  EXPONENTIAL SMOOTHING Exponential smoothing is a continuous adjustment process. The alpha α is used as the smoothing parameter to minimize the error and has a value of 0 to 1. The a is adjusted until the minimized mean squared error (MSE) is solved. If the difference between the actual value and the forecasted value is a positive, it means that the forecasted value is slow in reacting to the changes in sales increase and a higher or must be assigned. On the other hand, if the difference between the actual value and forecasted value is a negative, a lower a must be assigned. A higher a means that a greater weight is given to the most recent data and less weight to the distant past. EXPONENTIAL SMOOTHING Continuous Adjustment Process The Role of Alpha Behavior of α: Higher α Lower α  EXPONENTIAL SMOOTHING FORMULA:  TREND PROJECTION Trend projection technique fits a trend line to a series of historical data points and then projects the line into the future for medium-to-long range forecasts. The problem with this kind of technique is that it only visualizes the relationship of the given variables. Through this visualized relationship, a best fitting line is drawn through the observed data. Hence, doing a visual inspection suggests several trend lines that produce errors or vertical distances from the observed values to the plotted trend line. However, using the least squares method allows the user to find a line of best fir which will keep the errors to a minimum. This approach results in a straight line that minimizes the deviations between the observed values and the predicted values. The deviations or errors are the vertical distances between the observed values and the predicted values.  TREND PRROJECTION How It Works: 1. Visualizing the Relationship 2. Fitting a Trend Line 3. Visual Inspection 4. Using the Least Squares Method i. Deviations 5. Minimizing Errors  TREND PROJECTION KEY POINTS The trend line The least squares method Sudden changes and fluctuations  TREND PROJECTION SIMPLE PROBLEM Imagine you have data on yearly sales over the past five years. You can plot this data on a graph and notice that sales are generally increasing. A trend line can then be fitted to this data to represent the overall upward movement. By extending this trend line into the future, you can forecast future sales. The least squares method ensures that this trend line is the best fit, minimizing the errors between the actual sales and the predicted values from the trend line. ASSOCIATIVE MODEL Incorporates the variables or factors that might influence the quantity being forecasted. A type of predictive model that relies on the identification of relationships between variables. REGRESSION One of the most common methods. ANALYSIS The relationship between independent variables [predictors or features] and the dependent variable [outcome or target] is modeled mathematically. The goal of regression is to understand how changes in the independent variables are associated with changes in the dependent variable and to make predictions. LINEAR REGRESSION Formula: Shows the relationship between two variables: the independent and dependent variable WHERE: It assumes a linear relationship between the dependent variable and the independent variables. GETTING THE SLOPE The slope in a linear regression model indicates how much the dependent variable is expected to change for a one-unit change in the independent variable. FORMULA: GETTING THE SLOPE BOOK PROBLEM Advantages The linear equation provides a clear relationship between dependent and independent variables. Quantifies the relationship between variables, allowing for precise predictions. Each coefficient represents the impact of a unit change in the independent variable on the dependent variable, making it easy to understand the effect of different factors. Disadvantages Linear regression assumes a linear relationship between the dependent and independent variables. Linear regression assumes that each independent variable independently affects the dependent variable.  BSACC 3A FORECASTING THANK YOU FOR YOUR ATTENTION THIS IS GROUP 2