Song sale forecasting based on standalone music streaming a case study of spotify on popular US chart songs
| dc.contributor.author | Amayo, Oki Otambo | |
| dc.date.accessioned | 2022-02-01T12:55:19Z | |
| dc.date.available | 2022-02-01T12:55:19Z | |
| dc.date.issued | 2021 | |
| dc.description | Submitted in partial fulfillment of the requirements for the Degree of Bachelor of Business Science Finance at Strathmore University | en_US |
| dc.description.abstract | The recent rise in music streaming services and audiovisual platforms has facilitated listeners and music enthusiasts from all around the globe with even easier and cheaper access to music than ever before. However, this technological footprint has also had a significant impact onthe way music is marketed and distributed, thus affecting music revenues, record sales androyalty rates. Nevertheless, an ongoing debate seems to exist as to whether music streamingactually substitutes or complements record sales, with various research papers significantlybacking each view. This project seeks to add on to the existing literature by looking at a morerecent case study of Spotify in the United States. The plausible impact of streaming serviceson digital record sales is thus investigated using weekly data from June 2019 to June 2020,comprising the United States' Top 200 most streamed songs from Spotify as well as the Top100 most sold songs in the U.S. as observed by Rolling Stones. These variables are thenincorporated into two independent univariate time series models to account for time-lag effectsthat may exist in each, as well as investigate the plausible influence oftime on both variables.The causation effects between the two variables and their lagged forms are then subjected to a Granger-Causality test using a vector autoregressive model to investigate if there is in fact arelationship between them. Ultimately, the findings exhibited in this study seem to conclude that there is no relationship between streaming numbers and their equivalent record sales.Furthermore, both variables seem to exhibit random walk properties as both the time series models generated in this study do not seem to adequately describe the song sales and streaming paths, as demonstrated by the sample, suggesting that future values of both variables cannot accurately be predicted. From this, it is recommended that future research could instead try to focus on the effects of the two based on long-term data and instead focus on proving whether both the streaming numbers and song sale units do in fact follow a random walk process. | en_US |
| dc.identifier.uri | http://hdl.handle.net/11071/12550 | |
| dc.language.iso | en | en_US |
| dc.publisher | Strathmore University | en_US |
| dc.title | Song sale forecasting based on standalone music streaming a case study of spotify on popular US chart songs | en_US |
| dc.type | Undergraduate project | en_US |
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